Developing a reproducible approach for generating FCTs & NCTs

Technical webinar on the extension of the global food composition table for fisheries. November, 2022

Agenda (1)

  • Introduction

  • Objectives

  • Development of a framework for compiling NCTs. The Extended Fisheries Global NCT (2022) as case study

    • Background and starting point for the NCT
    • Harmonising Food Composition Tables and Databases
    • Visualisation for nutrient evaluation and missing values identification

Agenda (2)

  • The Extended Fisheries Global NCT (2022)

    • Food composition data for fishery products: Overview
    • Visualisation for decision making (missing values and outliers)
    • How scripted approach allow for fast update and re-use of the FCT for new purposes
    • How collaborative coding and GitHub increases efficiency and transparency of the approach
  • Conclusions & Recommendation for the future

    • What has been completed – what needs to be further advanced?
    • The need of better nutrition data for decision-making
    • New data skills and statistical approach for better food and nutrition data.
  • Q&A: Discussion & Next Steps

Introduction


Meet the team!

- Lucia Segovia de la Revilla
- Thomas Codd
- Liberty Mlambo
- Louise Ander

School of Biosciences

Objective


Applying MAPS framework:

to expand the Fisheries Global Nutrient Conversion Table (NCT)

The Fisheries Global Nutrient Conversion Table

The Fisheries Global Nutrient Conversion Table


The Fisheries Global NCT provided information on:

- 21 Food components.

- 95 ICS SUA Fisheries (and Aquatic Products) Categories.

- 11 Food composition tables and databases (FCTs).

The Fisheries Global Nutrient Conversion Table


The Fisheries Global NCT provided information on:

- 21 Food components + evaluate 10 fish-relevant food components.1

- 95 ICS SUA Fisheries (and Aquatic Products) Categories.

- 11 Food composition tables and databases (FCTs).

Development of the framework for compiling NCTs

Why the framework was needed?


  1. FCTs are available in multiple data formats.
  2. Data harmonisation requires effort, expertise, and time.
  3. Increasing the standardisation of the FCTs would…

... Lower cost of updating & generating new NCTs.

... Increase reproducibility of NCTs and their survey outputs.

... Increase re-usability & dissemination of FCTs.

Overview of the NCT framework

Steps 1 & 2: Data Sources & Importing


  • Downloading and storing original 11 FCTs 1 + Norwegian FCT (2021).

  • FCTs were imported into R(Studio) & independent scripts were generated for each FCT.

  • All the data operations (from Step 2 to Step 6) were done in R(Studio).

Step 3: Data cleaning and standardisation of FCTs


Food component names/ description standardisation using Tagnames1. For example…

“Vitamin B12” -> VITB12

“Vitamin A” -> VITA or VITA_RAE?
“Vitamin B6” -> VITB6A or VITB6C or VITB6-?

Step 3: Data cleaning and standardisation of FCTs


*Aquatic Sciences and Fisheries Information System

Step 3: Food name/ description standardisation


Fisheries categories matching in Norwegian FCT (2021).

  • Scientific names were used to allocate the ‘International Standard Statistical Classification for Aquatic Animals and Plants’ (ISSCAAP) codes.

  • ISSCAAP codes and text pattern (food description) to allocate ICS SUA Fisheries codes.

  • Review and final allocation of unidentified fish items were performed.

Step 4a: Data harmonisation and compilation of FCTs


  • Character values standardisation. For example, trace (“tr”) or below detection limit (“<LOD”) to zero (0) or removing special characters (“[]”, “()”, “*”).

  • Unit of measure standardisation. For example from mg/100g to g/100g.1

  • Harmonising Food Composition Tables and Databases. From 12 individual FCTs to a compiled FCT data library.

Step 4b: Variable recalculation and missing values


Other data transformation were performed in the harmonised data library.

  • Re-calculation of food components. For example, Energy, Carbohydrates by difference or Vitamin A (RAE).

  • Back-calculation from other components. For example, Niacin, preformed was back-calculated from Niacin, equivalents and Tryptophan.

  • Combining food components. For example, Vitamin B6 included: Vitamin B6 determined by analysis, determined by calculation and by unknown method.

  • Data imputation: For example, if a ICS SUA fishery category did not provided data for one nutrient.

Functions: Re-calculation


Functions were developed to re-calculate:

SOP_std_creator() - Sum of Proximate

ENERCKcal_standardised() - Energy (kcal)

CHOAVLDFg_std_creator() - Carbohydrates by difference
VITA_RAEmcg_std_creator() - Vitamin A (REA)
CARTBEQmcg_std_creator() - Beta-Carotene Eq.

Functions: Energy


ENERCKcal_standardised <- function(PROTg, FATg_standardised, CHOAVLDFg,
                          FIBGTg, ALCg){
                         
  ALCg <- ALCg %>% replace_na(0)
  FIBGTg <- FIBGTg %>% replace_na(0)
  ENERCKcal_std <- PROTg*4 + FATg_standardised*9 +  CHOAVLDFg*4 +
  FIBGTg*2 + ALCg*7
  
  return(ENERCKcal_std)
  
  }
  

Functions: Energy


ENERCKcal_standardised <- function(PROCNTg, FATg_standardised, CHOAVLDFg, 
                                   FIBGTg, ALCg){
                         
  ALCg <- ALCg %>% replace_na(0)
  FIBGTg <- FIBGTg %>% replace_na(0)
  ENERCKcal_std <- PROCNTg*4 + FATg_standardised*9 +  CHOAVLDFg*4 +
  FIBGTg*2 + ALCg*7
  
  return(ENERCKcal_std)
  
  }

Functions: Re-calculation


Functions were developed to re-calculate:

SOP_std_creator() - Sum of Proximate
ENERCKcal_standardised() - Energy (kcal)
CHOAVLDFg_std_creator() - Carbohydrates by difference
VITA_RAEmcg_std_creator() - Vitamin A (REA)

CARTBEQmcg_std_creator() - Beta-Carotene Eq.

Functions: Beta-Carotene Equivalents


CARTBEQmcg_std_creator <- function(dataset) {
    # Check presence of required columns
    columns <- c("CARTBmcg", "CARTAmcg", "CRYPXBmcg")
    check_columns(dataset = dataset, columns = columns)
    # Try the calculation
    tryCatch(
        dataset %>%
            as_tibble() %>%
            mutate_at(.vars = columns, .funs = as.numeric) %>%
            # ! Create a temp row with the count of NAs in the required columns
            mutate(temp = rowSums(is.na(
                dataset %>%
                    select(all_of(columns))
            ))) %>%
            rowwise() %>%
            # ! Replace comment NAs with blank so that we can concatenate comments well.
            mutate(comment = ifelse(is.na(comment), "", comment)) %>%
            # ! If all inputs to the CARTBEQmcg_std calculation are NA return NA
            # ! Else perform calculation ommiting NAs
            mutate(CARTBEQmcg_std = ifelse(
                temp == length(columns),
                NA,
                sum(1 * CARTBmcg, 0.5 * CARTAmcg, 0.5 * CRYPXBmcg, na.rm = TRUE)
            )) 
}

Functions: Beta-Carotene Equivalents


CARTBEQmcg_std_creator <- function(dataset) {
    # Check presence of required columns
    columns <- c("CARTBmcg", "CARTAmcg", "CRYPXBmcg")
    check_columns(dataset = dataset, columns = columns)
    # Try the calculation
    tryCatch(
        dataset %>%
            as_tibble() %>%
            mutate_at(.vars = columns, .funs = as.numeric) %>%
            # ! Create a temp row with the count of NAs in the required columns
            mutate(temp = rowSums(is.na(
                dataset %>%
                    select(all_of(columns))
            ))) %>%
            rowwise() %>%
            # ! Replace comment NAs with blank so that we can concatenate comments well.
            mutate(comment = ifelse(is.na(comment), "", comment)) %>%
            # ! If all inputs to the CARTBEQmcg_std calculation are NA return NA
            # ! Else perform calculation ommiting NAs
            mutate(CARTBEQmcg_std = ifelse(
                temp == length(columns),
                NA,
                sum(1 * CARTBmcg, 0.5 * CARTAmcg, 0.5 * CRYPXBmcg, na.rm = TRUE)
            )) %>%
            # ! Use the same logic as above for comment appendage.
            mutate(comment = ifelse(
                temp == length(columns),
                comment,
                paste0(
                    comment,
                    " | CARTBEQmcg_std calculated from CARTBmcg, CARTAmcg and CRYPXBmcg"
                )
            )) %>%
            # ! Check which components of the calculation were used. If only CARTB was used. Append the comment to reflect that.
            mutate(comment = ifelse(
                (
                    temp != length(columns) &
                        !is.na(CARTBmcg) &
                        is.na(CARTAmcg) & is.na(CRYPXBmcg)
                ),
                paste0(comment, " but only CARTB was used"),
                comment
            )) %>%
            # ! remove the temp column
            select(-temp) %>%
            ungroup(),
        error = function(e) {
            print("Error : Required columns not found i.e :")
            print(columns)
            print("The CARTBEQmcg_std will not be calculated")
        }
    )
}
  

Functions: Back-calculation


  1. Ash by difference (g/100g) - scripted

  2. Beta-carotene equivalents (mcg/100g) - CARTBEQmcg_std_back_calculator_VITA_RAEmcg()

  3. Retinol (mcg/100g) - RETOLmcg_Recalculator()

  4. Niacin (mcg/100g) - nia_conversion_creator()

Back-calculation: Ash by difference



Carbohydrates available by difference (g/100g) = 100 - (water + protein + fat + ash + alcohol + dietary fibre)


Ash by difference (g/100g) = 100 - (water + protein + fat + available carbohydrate + alcohol + dietary fibre)



NO_FCT_Data$ASHg_bydiff <-  100-(NO_FCT_Data$WATERg + NO_FCT_Data$PROCNTg + 
                          NO_FCT_Data$FAT_g + NO_FCT_Data$CHOAVLg +     
                          NO_FCT_Data$ALCg +  NO_FCT_Data$FIBTGg)
                                 

Combination of variables

  • Thiamin - THIAmg_standardised
  • Vitamin B6 - VITB6_mg_standardised
  • Fat - FAT_g_standardised
#This function combine all the Tagnames for FAT_g_standardised

for(i in 1:nrow(fao_fish_fct)){
  print(i)
  if (!is.na(fao_fish_fct$FATg[i])) {
    print(!is.na(fao_fish_fct$FATg[i]))
    fao_fish_fct$FAT_g_standardised[i] <- fao_fish_fct$FATg[i]
  }  
  if (is.na(fao_fish_fct$FATg[i])) { 
    fao_fish_fct$FAT_g_standardised[i] <- fao_fish_fct$FAT_g[i]
  } 
  if (is.na(fao_fish_fct$FATg[i]) & is.na(fao_fish_fct$FAT_g[i])) {
    fao_fish_fct$FAT_g_standardised[i] <- fao_fish_fct$FATCEg[i]
  }
  if (is.na(fao_fish_fct$FATg[i]) & is.na(fao_fish_fct$FAT_g[i]) & 
      is.na(fao_fish_fct$FATCEg[i])) {
    fao_fish_fct$FAT_g_standardised[i] <- NA
  }
  print(fao_fish_fct$FAT_g_standardised[i])
}
[1] 1
[1] TRUE
[1] "1.2"
[1] 2
[1] TRUE
[1] "0.7"
[1] 3
[1] "2.9"
[1] 4
[1] TRUE
[1] "3.064425770308123"
[1] 5
[1] TRUE
[1] "3.064425770308123"
[1] 6
[1] TRUE
[1] "3.5220588235294117"
[1] 7
[1] TRUE
[1] "3.5220588235294117"
[1] 8
[1] TRUE
[1] "3.8588235294117648"
[1] 9
[1] TRUE
[1] "3.8588235294117648"
[1] 10
[1] TRUE
[1] "1.411764705882353"
[1] 11
[1] TRUE
[1] "1.411764705882353"
[1] 12
[1] TRUE
[1] "1.6102941176470587"
[1] 13
[1] TRUE
[1] "1.6102941176470587"
[1] 14
[1] TRUE
[1] "1.5176470588235291"
[1] 15
[1] TRUE
[1] "3.1960784313725488"
[1] 16
[1] TRUE
[1] "3.1960784313725488"
[1] 17
[1] TRUE
[1] "2.0392156862745092"
[1] 18
[1] TRUE
[1] "2.0392156862745092"
[1] 19
[1] TRUE
[1] "2.1604278074866308"
[1] 20
[1] TRUE
[1] "2.1604278074866308"
[1] 21
[1] TRUE
[1] "1.4"
[1] 22
[1] TRUE
[1] "2.08"
[1] 23
[1] TRUE
[1] "1.6"
[1] 24
[1] TRUE
[1] "2.2000000000000002"
[1] 25
[1] TRUE
[1] "5.3"
[1] 26
[1] TRUE
[1] "5.3"
[1] 27
[1] TRUE
[1] "5.3"
[1] 28
[1] TRUE
[1] "5.3"
[1] 29
[1] TRUE
[1] "7.3"
[1] 30
[1] TRUE
[1] "7.3"
[1] 31
[1] TRUE
[1] "6.3"
[1] 32
[1] TRUE
[1] "6.3"
[1] 33
[1] TRUE
[1] "1.8"
[1] 34
[1] TRUE
[1] "1.8"
[1] 35
[1] TRUE
[1] "1.8"
[1] 36
[1] TRUE
[1] "1.8"
[1] 37
[1] TRUE
[1] "2.4"
[1] 38
[1] TRUE
[1] "2.4"
[1] 39
[1] TRUE
[1] "2.1"
[1] 40
[1] TRUE
[1] "2.1"
[1] 41
[1] TRUE
[1] "1.3"
[1] 42
[1] TRUE
[1] "1.3"
[1] 43
[1] TRUE
[1] "1.2"
[1] 44
[1] TRUE
[1] "1.2"
[1] 45
[1] TRUE
[1] "5.0999999999999996"
[1] 46
[1] TRUE
[1] "5.0999999999999996"
[1] 47
[1] TRUE
[1] "5.0999999999999996"
[1] 48
[1] TRUE
[1] "5.0999999999999996"
[1] 49
[1] TRUE
[1] "7"
[1] 50
[1] TRUE
[1] "7"
[1] 51
[1] TRUE
[1] "5.4"
[1] 52
[1] TRUE
[1] "5.4"
[1] 53
[1] TRUE
[1] "1.4"
[1] 54
[1] TRUE
[1] "1.4"
[1] 55
[1] TRUE
[1] "1.2"
[1] 56
[1] TRUE
[1] "1.2"
[1] 57
[1] TRUE
[1] "3.6"
[1] 58
[1] TRUE
[1] "3.6"
[1] 59
[1] TRUE
[1] "3.1"
[1] 60
[1] TRUE
[1] "3.1"
[1] 61
[1] TRUE
[1] "12.7"
[1] 62
[1] TRUE
[1] "12.7"
[1] 63
[1] TRUE
[1] "12.7"
[1] 64
[1] TRUE
[1] "12.7"
[1] 65
[1] TRUE
[1] "12.7"
[1] 66
[1] TRUE
[1] "12.7"
[1] 67
[1] TRUE
[1] "17.399999999999999"
[1] 68
[1] TRUE
[1] "17.399999999999999"
[1] 69
[1] TRUE
[1] "17.399999999999999"
[1] 70
[1] TRUE
[1] "15.1"
[1] 71
[1] TRUE
[1] "15.1"
[1] 72
[1] TRUE
[1] "15.1"
[1] 73
[1] TRUE
[1] "6.3"
[1] 74
[1] TRUE
[1] "6.3"
[1] 75
[1] TRUE
[1] "6.3"
[1] 76
[1] TRUE
[1] "6.3"
[1] 77
[1] TRUE
[1] "6.3"
[1] 78
[1] TRUE
[1] "6.3"
[1] 79
[1] TRUE
[1] "8.6"
[1] 80
[1] TRUE
[1] "8.6"
[1] 81
[1] TRUE
[1] "8.6"
[1] 82
[1] TRUE
[1] "7.5"
[1] 83
[1] TRUE
[1] "7.5"
[1] 84
[1] TRUE
[1] "7.5"
[1] 85
[1] TRUE
[1] "10.7"
[1] 86
[1] TRUE
[1] "10.7"
[1] 87
[1] TRUE
[1] "10.7"
[1] 88
[1] TRUE
[1] "10.7"
[1] 89
[1] TRUE
[1] "14.7"
[1] 90
[1] TRUE
[1] "14.7"
[1] 91
[1] TRUE
[1] "12.8"
[1] 92
[1] TRUE
[1] "12.8"
[1] 93
[1] TRUE
[1] "5.8"
[1] 94
[1] TRUE
[1] "5.8"
[1] 95
[1] TRUE
[1] "5.8"
[1] 96
[1] TRUE
[1] "5.8"
[1] 97
[1] TRUE
[1] "7.9"
[1] 98
[1] TRUE
[1] "7.9"
[1] 99
[1] TRUE
[1] "3.7"
[1] 100
[1] TRUE
[1] "3.7"
[1] 101
[1] TRUE
[1] "6"
[1] 102
[1] TRUE
[1] "6"
[1] 103
[1] TRUE
[1] "6"
[1] 104
[1] TRUE
[1] "6"
[1] 105
[1] TRUE
[1] "8.1999999999999993"
[1] 106
[1] TRUE
[1] "8.1999999999999993"
[1] 107
[1] TRUE
[1] "5"
[1] 108
[1] TRUE
[1] "5"
[1] 109
[1] TRUE
[1] "3.2"
[1] 110
[1] TRUE
[1] "3.2"
[1] 111
[1] TRUE
[1] "3.9"
[1] 112
[1] TRUE
[1] "3.9"
[1] 113
[1] TRUE
[1] "16.7"
[1] 114
[1] TRUE
[1] "16.7"
[1] 115
[1] TRUE
[1] "16.7"
[1] 116
[1] TRUE
[1] "16.7"
[1] 117
[1] TRUE
[1] "18.600000000000001"
[1] 118
[1] TRUE
[1] "18.600000000000001"
[1] 119
[1] TRUE
[1] "19.600000000000001"
[1] 120
[1] TRUE
[1] "19.600000000000001"
[1] 121
[1] TRUE
[1] "22.8"
[1] 122
[1] TRUE
[1] "22.8"
[1] 123
[1] TRUE
[1] "22.8"
[1] 124
[1] TRUE
[1] "22.8"
[1] 125
[1] TRUE
[1] "25.4"
[1] 126
[1] TRUE
[1] "25.4"
[1] 127
[1] TRUE
[1] "26.8"
[1] 128
[1] TRUE
[1] "26.8"
[1] 129
[1] TRUE
[1] "2.9"
[1] 130
[1] TRUE
[1] "2.9"
[1] 131
[1] TRUE
[1] "2.9"
[1] 132
[1] TRUE
[1] "2.9"
[1] 133
[1] TRUE
[1] "2.9"
[1] 134
[1] TRUE
[1] "2.9"
[1] 135
[1] TRUE
[1] "2.9"
[1] 136
[1] TRUE
[1] "2.9"
[1] 137
[1] TRUE
[1] "2.9"
[1] 138
[1] TRUE
[1] "2.9"
[1] 139
[1] TRUE
[1] "2.9"
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[1] TRUE
[1] "2.9"
[1] 141
[1] TRUE
[1] "1.9"
[1] 142
[1] TRUE
[1] "1.9"
[1] 143
[1] TRUE
[1] "1.9"
[1] 144
[1] TRUE
[1] "1.6"
[1] 145
[1] TRUE
[1] "1.6"
[1] 146
[1] TRUE
[1] "1.6"
[1] 147
[1] TRUE
[1] "1.6"
[1] 148
[1] TRUE
[1] "1.6"
[1] 149
[1] TRUE
[1] "1.6"
[1] 150
[1] TRUE
[1] "1.9"
[1] 151
[1] TRUE
[1] "1.9"
[1] 152
[1] TRUE
[1] "1.9"
[1] 153
[1] TRUE
[1] "0.4"
[1] 154
[1] TRUE
[1] "0.4"
[1] 155
[1] TRUE
[1] "0.4"
[1] 156
[1] TRUE
[1] "0.4"
[1] 157
[1] TRUE
[1] "0.6"
[1] 158
[1] TRUE
[1] "0.6"
[1] 159
[1] TRUE
[1] "0.5"
[1] 160
[1] TRUE
[1] "0.5"
[1] 161
[1] TRUE
[1] "14.5"
[1] 162
[1] TRUE
[1] "14.5"
[1] 163
[1] TRUE
[1] "16"
[1] 164
[1] TRUE
[1] "16"
[1] 165
[1] TRUE
[1] "16"
[1] 166
[1] TRUE
[1] "16"
[1] 167
[1] TRUE
[1] "22"
[1] 168
[1] TRUE
[1] "22"
[1] 169
[1] TRUE
[1] "19.100000000000001"
[1] 170
[1] TRUE
[1] "19.100000000000001"
[1] 171
[1] TRUE
[1] "1.6"
[1] 172
[1] TRUE
[1] "1.6"
[1] 173
[1] TRUE
[1] "1.6"
[1] 174
[1] TRUE
[1] "1.6"
[1] 175
[1] TRUE
[1] "2.2000000000000002"
[1] 176
[1] TRUE
[1] "2.2000000000000002"
[1] 177
[1] TRUE
[1] "2.2000000000000002"
[1] 178
[1] TRUE
[1] "2.2000000000000002"
[1] 179
[1] TRUE
[1] "4.4000000000000004"
[1] 180
[1] TRUE
[1] "4.4000000000000004"
[1] 181
[1] TRUE
[1] "4.4000000000000004"
[1] 182
[1] TRUE
[1] "4.4000000000000004"
[1] 183
[1] TRUE
[1] "6"
[1] 184
[1] TRUE
[1] "6"
[1] 185
[1] TRUE
[1] "5.2"
[1] 186
[1] TRUE
[1] "5.2"
[1] 187
[1] TRUE
[1] "24.6"
[1] 188
[1] TRUE
[1] "24.6"
[1] 189
[1] TRUE
[1] "24.6"
[1] 190
[1] TRUE
[1] "24.6"
[1] 191
[1] TRUE
[1] "33.700000000000003"
[1] 192
[1] TRUE
[1] "33.700000000000003"
[1] 193
[1] TRUE
[1] "29.3"
[1] 194
[1] TRUE
[1] "29.3"
[1] 195
[1] TRUE
[1] "8.4"
[1] 196
[1] TRUE
[1] "8.4"
[1] 197
[1] TRUE
[1] "8.4"
[1] 198
[1] TRUE
[1] "8.4"
[1] 199
[1] TRUE
[1] "11.6"
[1] 200
[1] TRUE
[1] "11.6"
[1] 201
[1] TRUE
[1] "10"
[1] 202
[1] TRUE
[1] "10"
[1] 203
[1] TRUE
[1] "1"
[1] 204
[1] TRUE
[1] "1"
[1] 205
[1] TRUE
[1] "1"
[1] 206
[1] TRUE
[1] "1"
[1] 207
[1] TRUE
[1] "1"
[1] 208
[1] TRUE
[1] "1"
[1] 209
[1] TRUE
[1] "1.4"
[1] 210
[1] TRUE
[1] "1.4"
[1] 211
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[1] "0.6"
[1] 640
[1] "0.6"
[1] 641
[1] "0.6"
[1] 642
[1] "0.6"
[1] 643
[1] "1.6"
[1] 644
[1] "1.6"
[1] 645
[1] "1.6"
[1] 646
[1] "1.6"
[1] 647
[1] "0.2"
[1] 648
[1] "0.2"
[1] 649
[1] "0.2"
[1] 650
[1] "0.2"
[1] 651
[1] "1.1000000000000001"
[1] 652
[1] "1.1000000000000001"
[1] 653
[1] "1.1000000000000001"
[1] 654
[1] "1.1000000000000001"
[1] 655
[1] "3"
[1] 656
[1] "1"
[1] 657
[1] "1"
[1] 658
[1] "1"
[1] 659
[1] "1"
[1] 660
[1] "1"
[1] 661
[1] "1"
[1] 662
[1] "3"
[1] 663
[1] "3"
[1] 664
[1] "3"
[1] 665
[1] "3"
[1] 666
[1] "2.4"
[1] 667
[1] "2.6"
[1] 668
[1] "1.2"
[1] 669
[1] "1.2"
[1] 670
[1] "1.2"
[1] 671
[1] "7.8"
[1] 672
[1] "7.8"
[1] 673
[1] "7.8"
[1] 674
[1] "7.8"
[1] 675
[1] "10.5"
[1] 676
[1] "10.5"
[1] 677
[1] "10.5"
[1] 678
[1] "10.5"
[1] 679
[1] "10.5"
[1] 680
[1] "10.5"
[1] 681
[1] "14.3"
[1] 682
[1] "14.3"
[1] 683
[1] "14.3"
[1] 684
[1] "14.3"
[1] 685
[1] "8.9"
[1] 686
[1] "8.9"
[1] 687
[1] "8.9"
[1] 688
[1] "8.9"
[1] 689
[1] "1.1000000000000001"
[1] 690
[1] "1.1000000000000001"
[1] 691
[1] "1.1000000000000001"
[1] 692
[1] "1.1000000000000001"
[1] 693
[1] "1.1000000000000001"
[1] 694
[1] "1.1000000000000001"
[1] 695
[1] "8.1999999999999993"
[1] 696
[1] "8.1999999999999993"
[1] 697
[1] "8"
[1] 698
[1] "8"
[1] 699
[1] "8"
[1] 700
[1] "10.3"
[1] 701
[1] "10.3"
[1] 702
[1] "10.3"
[1] 703
[1] "16"
[1] 704
[1] "16"
[1] 705
[1] "16"
[1] 706
[1] "16"
[1] 707
[1] "16"
[1] 708
[1] "16"
[1] 709
[1] "8.4"
[1] 710
[1] "8.4"
[1] 711
[1] "8.4"
[1] 712
[1] "16.100000000000001"
[1] 713
[1] "16.100000000000001"
[1] 714
[1] "14.9"
[1] 715
[1] "14.9"
[1] 716
[1] "14.9"
[1] 717
[1] "10.4"
[1] 718
[1] "10.4"
[1] 719
[1] "10.4"
[1] 720
[1] "16.399999999999999"
[1] 721
[1] "16.399999999999999"
[1] 722
[1] "16.399999999999999"
[1] 723
[1] "23.5"
[1] 724
[1] "23.5"
[1] 725
[1] "23.5"
[1] 726
[1] "10.3"
[1] 727
[1] "10.3"
[1] 728
[1] "10.3"
[1] 729
[1] "0.5"
[1] 730
[1] "25.3"
[1] 731
[1] "25.3"
[1] 732
[1] "25.3"
[1] 733
[1] "27.7"
[1] 734
[1] "27.7"
[1] 735
[1] "27.7"
[1] 736
[1] "27.7"
[1] 737
[1] "27.8"
[1] 738
[1] "27.8"
[1] 739
[1] "27.8"
[1] 740
[1] "100"
[1] 741
[1] "100"
[1] 742
[1] "100"
[1] 743
[1] "100"
[1] 744
[1] "100"
[1] 745
[1] "100"
[1] 746
[1] "100"
[1] 747
[1] "100"
[1] 748
[1] "99.9"
[1] 749
[1] "99.9"
[1] 750
[1] "99.9"
[1] 751
[1] "99.9"
[1] 752
[1] "99.9"
[1] 753
[1] "99.9"
[1] 754
[1] "99.9"
[1] 755
[1] "99.9"
[1] 756
[1] "8.9"
[1] 757
[1] "8.9"
[1] 758
[1] "8.9"
[1] 759
[1] "20.7"
[1] 760
[1] "20.7"
[1] 761
[1] "20.7"
[1] 762
[1] "1.7"
[1] 763
[1] "1.7"
[1] 764
[1] "1.7"
[1] 765
[1] "1.7"
[1] 766
[1] "1.7"
[1] 767
[1] "1.7"
[1] 768
[1] "18.3"
[1] 769
[1] "18.3"
[1] 770
[1] "18.3"
[1] 771
[1] "18.3"
[1] 772
[1] "2.2999999999999998"
[1] 773
[1] "2.2999999999999998"
[1] 774
[1] "2.2999999999999998"
[1] 775
[1] "2.2999999999999998"
[1] 776
[1] "0.98"
[1] 777
[1] "0.98"
[1] 778
[1] "1.8"
[1] 779
[1] "1.8"
[1] 780
[1] "1.8"
[1] 781
[1] "1.8"
[1] 782
[1] "0.78"
[1] 783
[1] "0.78"
[1] 784
[1] "0.78"
[1] 785
[1] "0.78"
[1] 786
[1] "1.11"
[1] 787
[1] "1.11"
[1] 788
[1] "0.29"
[1] 789
[1] "0.29"
[1] 790
[1] "0.29"
[1] 791
[1] "0.29"
[1] 792
[1] "1.03"
[1] 793
[1] "1.03"
[1] 794
[1] "2.87"
[1] 795
[1] "2.87"
[1] 796
[1] "2.13"
[1] 797
[1] "2.13"
[1] 798
[1] "1.68"
[1] 799
[1] "1.68"
[1] 800
[1] "1.68"
[1] 801
[1] "1.68"
[1] 802
[1] "0.67"
[1] 803
[1] "0.67"
[1] 804
[1] "0.7"
[1] 805
[1] "0.7"
[1] 806
[1] "0.7"
[1] 807
[1] "0.7"
[1] 808
[1] "2.02"
[1] 809
[1] "2.02"
[1] 810
[1] "0.88"
[1] 811
[1] "0.88"
[1] 812
[1] "0.44"
[1] 813
[1] "0.44"
[1] 814
[1] "18.49"
[1] 815
[1] "18.49"
[1] 816
[1] "18.49"
[1] 817
[1] "18.49"
[1] 818
[1] "1.46"
[1] 819
[1] "1.46"
[1] 820
[1] "1.92"
[1] 821
[1] "1.92"
[1] 822
[1] "1.34"
[1] 823
[1] "1.34"
[1] 824
[1] "4.2"
[1] 825
[1] "4.2"
[1] 826
[1] "4.54"
[1] 827
[1] "4.54"
[1] 828
[1] "1.2"
[1] 829
[1] "1.2"
[1] 830
[1] "1.27"
[1] 831
[1] "1.27"
[1] 832
[1] "1.27"
[1] 833
[1] "1.27"
[1] 834
[1] "1.38"
[1] 835
[1] "1.38"
[1] 836
[1] "1.38"
[1] 837
[1] "1.38"
[1] 838
[1] "0.84"
[1] 839
[1] "0.84"
[1] 840
[1] "0.84"
[1] 841
[1] "0.84"
[1] 842
[1] "3"
[1] 843
[1] "3"
[1] 844
[1] "3"
[1] 845
[1] "3"
[1] 846
[1] "4.7"
[1] 847
[1] "4.7"
[1] 848
[1] "4.7"
[1] 849
[1] "4.7"
[1] 850
[1] "0.56"
[1] 851
[1] "0.56"
[1] 852
[1] "3.36"
[1] 853
[1] "3.36"
[1] 854
[1] "3.6"
[1] 855
[1] "3.6"
[1] 856
[1] "3.6"
[1] 857
[1] "3.6"
[1] 858
[1] "0.67"
[1] 859
[1] "0.67"
[1] 860
[1] "1.53"
[1] 861
[1] "1.53"
[1] 862
[1] "1.53"
[1] 863
[1] "1.53"
[1] 864
[1] "0.79"
[1] 865
[1] "0.79"
[1] 866
[1] "1.09"
[1] 867
[1] "1.09"
[1] 868
[1] "1.09"
[1] 869
[1] "1.09"
[1] 870
[1] "4.69"
[1] 871
[1] "4.69"
[1] 872
[1] "1.32"
[1] 873
[1] "1.32"
[1] 874
[1] "0.58"
[1] 875
[1] "0.58"
[1] 876
[1] "0.58"
[1] 877
[1] "0.58"
[1] 878
[1] "0.56"
[1] 879
[1] "0.56"
[1] 880
[1] "0.56"
[1] 881
[1] "0.56"
[1] 882
[1] "0.72"
[1] 883
[1] "0.72"
[1] 884
[1] "1.6"
[1] 885
[1] "1.6"
[1] 886
[1] "1.6"
[1] 887
[1] "1.6"
[1] 888
[1] "1.84"
[1] 889
[1] "1.84"
[1] 890
[1] "1.84"
[1] 891
[1] "1.84"
[1] 892
[1] "0.69"
[1] 893
[1] "0.69"
[1] 894
[1] "1.81"
[1] 895
[1] "1.81"
[1] 896
[1] "4.44"
[1] 897
[1] "4.44"
[1] 898
[1] "2.74"
[1] 899
[1] "2.74"
[1] 900
[1] "2.74"
[1] 901
[1] "2.74"
[1] 902
[1] "0.62"
[1] 903
[1] "0.62"
[1] 904
[1] "0.57"
[1] 905
[1] "0.57"
[1] 906
[1] "0.87"
[1] 907
[1] "0.87"
[1] 908
[1] "1.33"
[1] 909
[1] "1.33"
[1] 910
[1] "1.33"
[1] 911
[1] "1.33"
[1] 912
[1] "4.83"
[1] 913
[1] "4.83"
[1] 914
[1] "4.83"
[1] 915
[1] "4.83"
[1] 916
[1] "0.46"
[1] 917
[1] "0.46"
[1] 918
[1] "0.46"
[1] 919
[1] "0.46"
[1] 920
[1] "5.12"
[1] 921
[1] "5.12"
[1] 922
[1] "5.12"
[1] 923
[1] "5.12"
[1] 924
[1] "1.89"
[1] 925
[1] "1.89"
[1] 926
[1] "1.55"
[1] 927
[1] "1.55"
[1] 928
[1] "1.55"
[1] 929
[1] "1.55"
[1] 930
[1] "1.17"
[1] 931
[1] "1.17"
[1] 932
[1] "1.17"
[1] 933
[1] "1.17"
[1] 934
[1] "1.69"
[1] 935
[1] "1.69"
[1] 936
[1] "2.17"
[1] 937
[1] "2.17"
[1] 938
[1] "0.7"
[1] 939
[1] "0.7"
[1] 940
[1] "1.34"
[1] 941
[1] "1.34"
[1] 942
[1] "2.3"
[1] 943
[1] "2.3"
[1] 944
[1] "2.97"
[1] 945
[1] "2.97"
[1] 946
[1] "9.86"
[1] 947
[1] "9.86"
[1] 948
[1] "9.86"
[1] 949
[1] "9.86"
[1] 950
[1] "2.67"
[1] 951
[1] "2.67"
[1] 952
[1] "1.68"
[1] 953
[1] "1.68"
[1] 954
[1] "1.68"
[1] 955
[1] "1.68"
[1] 956
[1] "8.99"
[1] 957
[1] "8.99"
[1] 958
[1] "8.99"
[1] 959
[1] "8.99"
[1] 960
[1] "0.83"
[1] 961
[1] "0.83"
[1] 962
[1] "0.8"
[1] 963
[1] "0.8"
[1] 964
[1] "0.75"
[1] 965
[1] "0.75"
[1] 966
[1] "1.74"
[1] 967
[1] "1.74"
[1] 968
[1] "1.74"
[1] 969
[1] "1.74"
[1] 970
[1] "6.68"
[1] 971
[1] "6.68"
[1] 972
[1] "6.68"
[1] 973
[1] "6.68"
[1] 974
[1] "1.53"
[1] 975
[1] "1.53"
[1] 976
[1] "5.17"
[1] 977
[1] "5.17"
[1] 978
[1] "5.17"
[1] 979
[1] "5.17"
[1] 980
[1] "1.18"
[1] 981
[1] "1.18"
[1] 982
[1] "0.68"
[1] 983
[1] "0.68"
[1] 984
[1] "1.2"
[1] 985
[1] "1.2"
[1] 986
[1] "2.13"
[1] 987
[1] "2.13"
[1] 988
[1] "1.08"
[1] 989
[1] "1.08"
[1] 990
[1] "1.08"
[1] 991
[1] "1.08"
[1] 992
[1] "1.44"
[1] 993
[1] "1.44"
[1] 994
[1] "1.44"
[1] 995
[1] "1.44"
[1] 996
[1] "1.12"
[1] 997
[1] "1.12"
[1] 998
[1] "1.12"
[1] 999
[1] "1.12"
[1] 1000
[1] "1.06"
[1] 1001
[1] "1.06"
[1] 1002
[1] "1.06"
[1] 1003
[1] "1.06"
[1] 1004
[1] "5.18"
[1] 1005
[1] "5.18"
[1] 1006
[1] "5.18"
[1] 1007
[1] "5.18"
[1] 1008
[1] "4.34"
[1] 1009
[1] "4.34"
[1] 1010
[1] "2.14"
[1] 1011
[1] "2.14"
[1] 1012
[1] "0.82"
[1] 1013
[1] "0.82"
[1] 1014
[1] "0.88"
[1] 1015
[1] "0.88"
[1] 1016
[1] "0.88"
[1] 1017
[1] "0.88"
[1] 1018
[1] "1.41"
[1] 1019
[1] "1.41"
[1] 1020
[1] "0.6"
[1] 1021
[1] "0.6"
[1] 1022
[1] "0.56"
[1] 1023
[1] "0.56"
[1] 1024
[1] "0.78"
[1] 1025
[1] "0.78"
[1] 1026
[1] "0.55"
[1] 1027
[1] "0.55"
[1] 1028
[1] "2.44"
[1] 1029
[1] "2.44"
[1] 1030
[1] "0.56"
[1] 1031
[1] "0.56"
[1] 1032
[1] "0.74"
[1] 1033
[1] "0.74"
[1] 1034
[1] "0.98"
[1] 1035
[1] "0.98"
[1] 1036
[1] "1.34"
[1] 1037
[1] "1.34"
[1] 1038
[1] "1.12"
[1] 1039
[1] "1.12"
[1] 1040
[1] "1.07"
[1] 1041
[1] "1.07"
[1] 1042
[1] "0.93"
[1] 1043
[1] "0.93"
[1] 1044
[1] "1.44"
[1] 1045
[1] "1.44"
[1] 1046
[1] "1.15"
[1] 1047
[1] "1.15"
[1] 1048
[1] "6.24"
[1] 1049
[1] "6.24"
[1] 1050
[1] "6.24"
[1] 1051
[1] "6.24"
[1] 1052
[1] "2.15"
[1] 1053
[1] "2.15"
[1] 1054
[1] "2.15"
[1] 1055
[1] "2.15"
[1] 1056
[1] "2.63"
[1] 1057
[1] "2.63"
[1] 1058
[1] "2.63"
[1] 1059
[1] "2.63"
[1] 1060
[1] "2.94"
[1] 1061
[1] "2.94"
[1] 1062
[1] "2.94"
[1] 1063
[1] "2.94"
[1] 1064
[1] "16.77"
[1] 1065
[1] "16.77"
[1] 1066
[1] "16.77"
[1] 1067
[1] "16.77"
[1] 1068
[1] "2.39"
[1] 1069
[1] "2.39"
[1] 1070
[1] "2.39"
[1] 1071
[1] "2.39"
[1] 1072
[1] "0.89"
[1] 1073
[1] "0.89"
[1] 1074
[1] "0.52"
[1] 1075
[1] "0.52"
[1] 1076
[1] "0.78"
[1] 1077
[1] "0.78"
[1] 1078
[1] "0.66"
[1] 1079
[1] "0.66"
[1] 1080
[1] TRUE
[1] "0.6"
[1] 1081
[1] TRUE
[1] "5.2"
[1] 1082
[1] TRUE
[1] "3.7"
[1] 1083
[1] TRUE
[1] "0.7"
[1] 1084
[1] TRUE
[1] "0.7"
[1] 1085
[1] TRUE
[1] "0.1"
[1] 1086
[1] TRUE
[1] "0.1"
[1] 1087
[1] TRUE
[1] "1.6"
[1] 1088
[1] TRUE
[1] "1.0"
[1] 1089
[1] TRUE
[1] "0.5"
[1] 1090
[1] TRUE
[1] "1.5"
[1] 1091
[1] TRUE
[1] "1.7"
[1] 1092
[1] TRUE
[1] "1.9"
[1] 1093
[1] TRUE
[1] "2.0"
[1] 1094
[1] TRUE
[1] "0.5"
[1] 1095
[1] TRUE
[1] "0.9"
[1] 1096
[1] TRUE
[1] "1.0"
[1] 1097
[1] TRUE
[1] "0.2"
[1] 1098
[1] TRUE
[1] "0.3"
[1] 1099
[1] TRUE
[1] "3.2"
[1] 1100
[1] TRUE
[1] "3.2"
[1] 1101
[1] TRUE
[1] "1.0"
[1] 1102
[1] TRUE
[1] "1.0"
[1] 1103
[1] TRUE
[1] "4.9000000000000004"
[1] 1104
[1] TRUE
[1] "0.2"
[1] 1105
[1] TRUE
[1] "1.6"
[1] 1106
[1] TRUE
[1] "1.2"
[1] 1107
[1] TRUE
[1] "4.0"
[1] 1108
[1] TRUE
[1] "0.6"
[1] 1109
[1] TRUE
[1] "4.5"
[1] 1110
[1] TRUE
[1] "4.5"
[1] 1111
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1213
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[1] TRUE
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[1] 1222
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1230
[1] TRUE
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[1] 1231
[1] TRUE
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[1] 1232
[1] TRUE
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[1] 1233
[1] TRUE
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[1] 1234
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1238
[1] TRUE
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[1] 1239
[1] TRUE
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[1] 1240
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1302
[1] TRUE
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[1] 1303
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1320
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1323
[1] TRUE
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[1] TRUE
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[1] 1325
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1330
[1] TRUE
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[1] 1332
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1340
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1343
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1348
[1] TRUE
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[1] TRUE
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[1] 1350
[1] TRUE
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[1] TRUE
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[1] 1353
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1360
[1] TRUE
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[1] TRUE
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[1] 1362
[1] TRUE
[1] "24.2"
[1] 1363
[1] TRUE
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[1] 1364
[1] TRUE
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[1] 1365
[1] TRUE
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[1] 1366
[1] TRUE
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[1] TRUE
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[1] 1368
[1] TRUE
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[1] TRUE
[1] "4.9000000000000004"
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[1] TRUE
[1] "4.9000000000000004"
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[1] TRUE
[1] "4.9000000000000004"
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 1383
[1] TRUE
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[1] TRUE
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[1] 1385
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
[1] "18.600000000000001"
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[1] TRUE
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[1] TRUE
[1] "18.600000000000001"
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[1] TRUE
[1] "18.600000000000001"
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[1] TRUE
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[1] 1413
[1] TRUE
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[1] 1414
[1] TRUE
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[1] 1415
[1] TRUE
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[1] 1416
[1] TRUE
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[1] 1417
[1] TRUE
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[1] 1418
[1] TRUE
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[1] 1419
[1] TRUE
[1] "0.8"
[1] 1420
[1] TRUE
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[1] 1421
[1] TRUE
[1] "10.199999999999999"
[1] 1422
[1] TRUE
[1] "10.199999999999999"
[1] 1423
[1] TRUE
[1] "10.199999999999999"
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[1] TRUE
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[1] 1425
[1] TRUE
[1] "13.4"
[1] 1426
[1] TRUE
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[1] 1427
[1] TRUE
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[1] 1428
[1] TRUE
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[1] 1429
[1] TRUE
[1] "0.5"
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[1] TRUE
[1] "8.3000000000000007"
[1] 1431
[1] TRUE
[1] "8.3000000000000007"
[1] 1432
[1] TRUE
[1] "8.3000000000000007"
[1] 1433
[1] TRUE
[1] "8.3000000000000007"
[1] 1434
[1] TRUE
[1] "10.1"
[1] 1435
[1] TRUE
[1] "10.1"
[1] 1436
[1] TRUE
[1] "6.6"
[1] 1437
[1] TRUE
[1] "6.6"
[1] 1438
[1] TRUE
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[1] 1439
[1] TRUE
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[1] 1440
[1] TRUE
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[1] 1441
[1] TRUE
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[1] 1442
[1] TRUE
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[1] TRUE
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[1] 1444
[1] TRUE
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[1] 1445
[1] TRUE
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[1] 1446
[1] TRUE
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[1] 1447
[1] TRUE
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[1] 1448
[1] TRUE
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[1] 1449
[1] TRUE
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[1] 1450
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[1] 1451
[1] TRUE
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[1] 1452
[1] TRUE
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[1] 1453
[1] TRUE
[1] "7.7"
[1] 1454
[1] TRUE
[1] "12.0"
[1] 1455
[1] TRUE
[1] "12.0"
[1] 1456
[1] TRUE
[1] "4.0999999999999996"
[1] 1457
[1] TRUE
[1] "4.0999999999999996"
[1] 1458
[1] TRUE
[1] "4.0999999999999996"
[1] 1459
[1] TRUE
[1] "4.0999999999999996"
[1] 1460
[1] TRUE
[1] "4.7"
[1] 1461
[1] TRUE
[1] "4.7"
[1] 1462
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[1] 1800
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[1] 1805
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[1] 1806
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[1] 1807
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[1] 1811
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[1] 1812
[1] TRUE
[1] "0.7"
[1] 1813
[1] TRUE
[1] "0.7"
[1] 1814
[1] TRUE
[1] "0.1"
[1] 1815
[1] TRUE
[1] "0.1"
[1] 1816
[1] TRUE
[1] "0.1"
[1] 1817
[1] TRUE
[1] "0.1"
[1] 1818
[1] TRUE
[1] "0.1"
[1] 1819
[1] TRUE
[1] "0.1"
[1] 1820
[1] TRUE
[1] "28.3"
[1] 1821
[1] TRUE
[1] "28.3"
[1] 1822
[1] TRUE
[1] "28.3"
[1] 1823
[1] TRUE
[1] "28.3"
[1] 1824
[1] TRUE
[1] "28.3"
[1] 1825
[1] TRUE
[1] "28.3"
[1] 1826
[1] TRUE
[1] "4.8"
[1] 1827
[1] TRUE
[1] "4.8"
[1] 1828
[1] TRUE
[1] "4.8"
[1] 1829
[1] TRUE
[1] "4.8"
[1] 1830
[1] TRUE
[1] "4.8"
[1] 1831
[1] TRUE
[1] "4.8"
[1] 1832
[1] TRUE
[1] "1.2"
[1] 1833
[1] TRUE
[1] "1.2"
[1] 1834
[1] TRUE
[1] "1.2"
[1] 1835
[1] TRUE
[1] "1.2"
[1] 1836
[1] TRUE
[1] "1.2"
[1] 1837
[1] TRUE
[1] "1.2"
[1] 1838
[1] TRUE
[1] "0.7"
[1] 1839
[1] TRUE
[1] "0.7"
[1] 1840
[1] TRUE
[1] "0.7"
[1] 1841
[1] TRUE
[1] "2.5"
[1] 1842
[1] TRUE
[1] "2.5"
[1] 1843
[1] TRUE
[1] "2.5"
[1] 1844
[1] TRUE
[1] "2.2999999999999998"
[1] 1845
[1] TRUE
[1] "2.2999999999999998"
[1] 1846
[1] TRUE
[1] "2.2999999999999998"
[1] 1847
[1] TRUE
[1] "21.7"
[1] 1848
[1] TRUE
[1] "21.7"
[1] 1849
[1] TRUE
[1] "21.7"
[1] 1850
[1] TRUE
[1] "23.6"
[1] 1851
[1] TRUE
[1] "23.6"
[1] 1852
[1] TRUE
[1] "23.6"
[1] 1853
[1] TRUE
[1] "22.9"
[1] 1854
[1] TRUE
[1] "22.9"
[1] 1855
[1] TRUE
[1] "22.9"
[1] 1856
[1] TRUE
[1] "22.9"
[1] 1857
[1] TRUE
[1] "10.9"
[1] 1858
[1] TRUE
[1] "10.9"
[1] 1859
[1] TRUE
[1] "10.9"
[1] 1860
[1] TRUE
[1] "10.9"
[1] 1861
[1] TRUE
[1] "10.9"
[1] 1862
[1] TRUE
[1] "10.9"
[1] 1863
[1] TRUE
[1] "3.0"
[1] 1864
[1] TRUE
[1] "3.0"
[1] 1865
[1] TRUE
[1] "3.0"
[1] 1866
[1] TRUE
[1] "3.0"
[1] 1867
[1] TRUE
[1] "0.3"
[1] 1868
[1] TRUE
[1] "0.3"
[1] 1869
[1] TRUE
[1] "0.3"
[1] 1870
[1] TRUE
[1] "0.3"
[1] 1871
[1] TRUE
[1] "12.6"
[1] 1872
[1] TRUE
[1] "12.6"
[1] 1873
[1] TRUE
[1] "12.6"
[1] 1874
[1] TRUE
[1] "12.6"
[1] 1875
[1] TRUE
[1] "8.4"
[1] 1876
[1] TRUE
[1] "8.4"
[1] 1877
[1] TRUE
[1] "4.5"
[1] 1878
[1] TRUE
[1] "4.5"
[1] 1879
[1] TRUE
[1] "4.5"
[1] 1880
[1] TRUE
[1] "4.5"
[1] 1881
[1] TRUE
[1] "3.5"
[1] 1882
[1] TRUE
[1] "3.5"
[1] 1883
[1] TRUE
[1] "3.5"
[1] 1884
[1] TRUE
[1] "3.5"
[1] 1885
[1] TRUE
[1] "0.6"
[1] 1886
[1] TRUE
[1] "0.6"
[1] 1887
[1] TRUE
[1] "0.6"
[1] 1888
[1] TRUE
[1] "0.6"
[1] 1889
[1] TRUE
[1] "21.8"
[1] 1890
[1] TRUE
[1] "21.8"
[1] 1891
[1] TRUE
[1] "21.8"
[1] 1892
[1] TRUE
[1] "21.8"
[1] 1893
[1] TRUE
[1] "4.3"
[1] 1894
[1] TRUE
[1] "4.3"
[1] 1895
[1] TRUE
[1] "4.3"
[1] 1896
[1] TRUE
[1] "4.3"
[1] 1897
[1] TRUE
[1] "1.7"
[1] 1898
[1] TRUE
[1] "1.7"
[1] 1899
[1] TRUE
[1] "1.7"
[1] 1900
[1] TRUE
[1] "1.7"
[1] 1901
[1] TRUE
[1] "5.5"
[1] 1902
[1] TRUE
[1] "5.5"
[1] 1903
[1] TRUE
[1] "5.0999999999999996"
[1] 1904
[1] TRUE
[1] "5.0999999999999996"
[1] 1905
[1] TRUE
[1] "0.3"
[1] 1906
[1] TRUE
[1] "0.3"
[1] 1907
[1] TRUE
[1] "0.6"
[1] 1908
[1] TRUE
[1] "0.6"
[1] 1909
[1] TRUE
[1] "0.3"
[1] 1910
[1] TRUE
[1] "0.3"
[1] 1911
[1] TRUE
[1] "2.2000000000000002"
[1] 1912
[1] TRUE
[1] "0.3"
[1] 1913
[1] TRUE
[1] "0.3"
[1] 1914
[1] TRUE
[1] "1.6"
[1] 1915
[1] TRUE
[1] "0.4"
[1] 1916
[1] TRUE
[1] "1.4"
[1] 1917
[1] TRUE
[1] "1.4"
[1] 1918
[1] TRUE
[1] "0.8"
[1] 1919
[1] TRUE
[1] "0.8"
[1] 1920
[1] TRUE
[1] "1.0"
[1] 1921
[1] TRUE
[1] "1.0"
[1] 1922
[1] TRUE
[1] "1.4"
[1] 1923
[1] TRUE
[1] "1.4"
[1] 1924
[1] TRUE
[1] "0.4"
[1] 1925
[1] TRUE
[1] "0.4"
[1] 1926
[1] TRUE
[1] "1.4"
[1] 1927
[1] TRUE
[1] "1.4"
[1] 1928
[1] TRUE
[1] "0.2"
[1] 1929
[1] TRUE
[1] "0.2"
[1] 1930
[1] TRUE
[1] "0.2"
[1] 1931
[1] TRUE
[1] "0.2"
[1] 1932
[1] TRUE
[1] "0.4"
[1] 1933
[1] TRUE
[1] "0.4"
[1] 1934
[1] TRUE
[1] "0.3"
[1] 1935
[1] TRUE
[1] "0.3"
[1] 1936
[1] TRUE
[1] "0.6"
[1] 1937
[1] TRUE
[1] "0.6"
[1] 1938
[1] TRUE
[1] "0.5"
[1] 1939
[1] TRUE
[1] "0.5"
[1] 1940
[1] TRUE
[1] "0.6"
[1] 1941
[1] TRUE
[1] "0.6"
[1] 1942
[1] TRUE
[1] "0.4"
[1] 1943
[1] TRUE
[1] "0.4"
[1] 1944
[1] TRUE
[1] "0.9"
[1] 1945
[1] TRUE
[1] "0.9"
[1] 1946
[1] TRUE
[1] "0.3"
[1] 1947
[1] TRUE
[1] "0.3"
[1] 1948
[1] TRUE
[1] "1.4"
[1] 1949
[1] TRUE
[1] "0.6"
[1] 1950
[1] TRUE
[1] "1.1000000000000001"
[1] 1951
[1] TRUE
[1] "1.1000000000000001"
[1] 1952
[1] TRUE
[1] "0.4"
[1] 1953
[1] TRUE
[1] "0.4"
[1] 1954
[1] TRUE
[1] "0.3"
[1] 1955
[1] TRUE
[1] "0.3"
[1] 1956
[1] TRUE
[1] "0.4"
[1] 1957
[1] TRUE
[1] "0.4"
[1] 1958
[1] TRUE
[1] "0.6"
[1] 1959
[1] TRUE
[1] "0.6"
[1] 1960
[1] TRUE
[1] "0.5"
[1] 1961
[1] TRUE
[1] "0.4"
[1] 1962
[1] TRUE
[1] "1.5"
[1] 1963
[1] TRUE
[1] "4.0"
[1] 1964
[1] TRUE
[1] "0.3"
[1] 1965
[1] TRUE
[1] "0.3"
[1] 1966
[1] TRUE
[1] "0.4"
[1] 1967
[1] TRUE
[1] "0.4"
[1] 1968
[1] TRUE
[1] "0.6"
[1] 1969
[1] TRUE
[1] "0.6"
[1] 1970
[1] TRUE
[1] "10.3"
[1] 1971
[1] TRUE
[1] "0.3"
[1] 1972
[1] TRUE
[1] "0.3"
[1] 1973
[1] TRUE
[1] "0.3"
[1] 1974
[1] TRUE
[1] "0.3"
[1] 1975
[1] TRUE
[1] "0.5"
[1] 1976
[1] TRUE
[1] "0.5"
[1] 1977
[1] TRUE
[1] "0.5"
[1] 1978
[1] TRUE
[1] "0.4"
[1] 1979
[1] TRUE
[1] "0.4"
[1] 1980
[1] TRUE
[1] "0.6"
[1] 1981
[1] TRUE
[1] "0.4"
[1] 1982
[1] TRUE
[1] "0.3"
[1] 1983
[1] TRUE
[1] "0.3"
[1] 1984
[1] TRUE
[1] "0.5"
[1] 1985
[1] TRUE
[1] "0.3"
[1] 1986
[1] TRUE
[1] "1.4"
[1] 1987
[1] TRUE
[1] "1.4"
[1] 1988
[1] TRUE
[1] "1.0"
[1] 1989
[1] TRUE
[1] "1.0"
[1] 1990
[1] TRUE
[1] "0.3"
[1] 1991
[1] TRUE
[1] "0.3"
[1] 1992
[1] TRUE
[1] "0.8"
[1] 1993
[1] TRUE
[1] "0.8"
[1] 1994
[1] TRUE
[1] "0.9"
[1] 1995
[1] TRUE
[1] "1.0"
[1] 1996
[1] TRUE
[1] "0.7"
[1] 1997
[1] TRUE
[1] "0.7"
[1] 1998
[1] TRUE
[1] "0.6"
[1] 1999
[1] TRUE
[1] "10.8"
[1] 2000
[1] TRUE
[1] "0.9"
[1] 2001
[1] TRUE
[1] "0.9"
[1] 2002
[1] TRUE
[1] "3.5"
[1] 2003
[1] TRUE
[1] "3.5"
[1] 2004
[1] TRUE
[1] "2.9"
[1] 2005
[1] TRUE
[1] "7.5"
[1] 2006
[1] TRUE
[1] "1.0"
[1] 2007
[1] TRUE
[1] "1.0"
[1] 2008
[1] TRUE
[1] "4.3"
[1] 2009
[1] TRUE
[1] "3.1"
[1] 2010
[1] TRUE
[1] "1.5"
[1] 2011
[1] TRUE
[1] "0.8"
[1] 2012
[1] TRUE
[1] "0.8"
[1] 2013
[1] TRUE
[1] "0.7"
[1] 2014
[1] TRUE
[1] "0.7"
[1] 2015
[1] TRUE
[1] "0.7"
[1] 2016
[1] TRUE
[1] "4.8"
[1] 2017
[1] TRUE
[1] "5.8"
[1] 2018
[1] TRUE
[1] "2.9"
[1] 2019
[1] TRUE
[1] "3.0"
[1] 2020
[1] TRUE
[1] "0.1"
[1] 2021
[1] TRUE
[1] "1.7"
[1] 2022
[1] TRUE
[1] "0.3"
[1] 2023
[1] TRUE
[1] "1.8"
[1] 2024
[1] TRUE
[1] "0.8"
[1] 2025
[1] TRUE
[1] "1.1000000000000001"
[1] 2026
[1] TRUE
[1] "0.4"
[1] 2027
[1] TRUE
[1] "31.4"
[1] 2028
[1] TRUE
[1] "68.8"
[1] 2029
[1] TRUE
[1] "0.9"
[1] 2030
[1] TRUE
[1] "0.6"
[1] 2031
[1] TRUE
[1] "0.6"
[1] 2032
[1] TRUE
[1] "0.6"
[1] 2033
[1] TRUE
[1] "0.6"
[1] 2034
[1] TRUE
[1] "0.7"
[1] 2035
[1] TRUE
[1] "0.7"
[1] 2036
[1] TRUE
[1] "0.8"
[1] 2037
[1] TRUE
[1] "0.8"
[1] 2038
[1] TRUE
[1] "0.7"
[1] 2039
[1] TRUE
[1] "0.7"
[1] 2040
[1] TRUE
[1] "4.8"
[1] 2041
[1] TRUE
[1] "4.8"
[1] 2042
[1] TRUE
[1] "11.7"
[1] 2043
[1] TRUE
[1] "11.7"
[1] 2044
[1] TRUE
[1] "11.7"
[1] 2045
[1] TRUE
[1] "11.7"
[1] 2046
[1] TRUE
[1] "12.6"
[1] 2047
[1] TRUE
[1] "12.6"
[1] 2048
[1] TRUE
[1] "13.1"
[1] 2049
[1] TRUE
[1] "13.1"
[1] 2050
[1] TRUE
[1] "14"
[1] 2051
[1] TRUE
[1] "14"
[1] 2052
[1] TRUE
[1] "4.3"
[1] 2053
[1] TRUE
[1] "4.3"
[1] 2054
[1] TRUE
[1] "4.3"
[1] 2055
[1] TRUE
[1] "4.3"
[1] 2056
[1] TRUE
[1] "5.2"
[1] 2057
[1] TRUE
[1] "5.2"
[1] 2058
[1] TRUE
[1] "5.6"
[1] 2059
[1] TRUE
[1] "5.6"
[1] 2060
[1] TRUE
[1] "5.2"
[1] 2061
[1] TRUE
[1] "5.2"
[1] 2062
[1] TRUE
[1] "1"
[1] 2063
[1] TRUE
[1] "1"
[1] 2064
[1] TRUE
[1] "1"
[1] 2065
[1] TRUE
[1] "1"
[1] 2066
[1] TRUE
[1] "1.2"
[1] 2067
[1] TRUE
[1] "1.2"
[1] 2068
[1] TRUE
[1] "1.3"
[1] 2069
[1] TRUE
[1] "1.3"
[1] 2070
[1] TRUE
[1] "1.2"
[1] 2071
[1] TRUE
[1] "1.2"
[1] 2072
[1] TRUE
[1] "3.5"
[1] 2073
[1] TRUE
[1] "3.5"
[1] 2074
[1] TRUE
[1] "3.5"
[1] 2075
[1] TRUE
[1] "3.5"
[1] 2076
[1] TRUE
[1] "4.2"
[1] 2077
[1] TRUE
[1] "4.2"
[1] 2078
[1] TRUE
[1] "4.5"
[1] 2079
[1] TRUE
[1] "4.5"
[1] 2080
[1] TRUE
[1] "4.2"
[1] 2081
[1] TRUE
[1] "4.2"
[1] 2082
[1] TRUE
[1] "5.9"
[1] 2083
[1] TRUE
[1] "5.9"
[1] 2084
[1] TRUE
[1] "5.9"
[1] 2085
[1] TRUE
[1] "5.9"
[1] 2086
[1] TRUE
[1] "5.9"
[1] 2087
[1] TRUE
[1] "5.9"
[1] 2088
[1] TRUE
[1] "0.9"
[1] 2089
[1] TRUE
[1] "0.9"
[1] 2090
[1] TRUE
[1] "0.9"
[1] 2091
[1] TRUE
[1] "0.9"
[1] 2092
[1] TRUE
[1] "1.5"
[1] 2093
[1] TRUE
[1] "1.5"
[1] 2094
[1] TRUE
[1] "1.5"
[1] 2095
[1] TRUE
[1] "1.5"
[1] 2096
[1] TRUE
[1] "1.1"
[1] 2097
[1] TRUE
[1] "1.1"
[1] 2098
[1] TRUE
[1] "1.3"
[1] 2099
[1] TRUE
[1] "1.3"
[1] 2100
[1] TRUE
[1] "7.4"
[1] 2101
[1] TRUE
[1] "7.4"
[1] 2102
[1] TRUE
[1] "7.4"
[1] 2103
[1] TRUE
[1] "7.4"
[1] 2104
[1] TRUE
[1] "7.4"
[1] 2105
[1] TRUE
[1] "7.4"
[1] 2106
[1] TRUE
[1] "7.4"
[1] 2107
[1] TRUE
[1] "7.4"
[1] 2108
[1] TRUE
[1] "7.4"
[1] 2109
[1] TRUE
[1] "7.5"
[1] 2110
[1] TRUE
[1] "7.5"
[1] 2111
[1] TRUE
[1] "7.5"
[1] 2112
[1] TRUE
[1] "8.7"
[1] 2113
[1] TRUE
[1] "8.7"
[1] 2114
[1] TRUE
[1] "8.7"
[1] 2115
[1] TRUE
[1] "0.8"
[1] 2116
[1] TRUE
[1] "0.8"
[1] 2117
[1] TRUE
[1] "0.8"
[1] 2118
[1] TRUE
[1] "0.8"
[1] 2119
[1] TRUE
[1] "1"
[1] 2120
[1] TRUE
[1] "1"
[1] 2121
[1] TRUE
[1] "1.1"
[1] 2122
[1] TRUE
[1] "1.1"
[1] 2123
[1] TRUE
[1] "1"
[1] 2124
[1] TRUE
[1] "1"
[1] 2125
[1] TRUE
[1] "2"
[1] 2126
[1] TRUE
[1] "2"
[1] 2127
[1] TRUE
[1] "2"
[1] 2128
[1] TRUE
[1] "2"
[1] 2129
[1] TRUE
[1] "2"
[1] 2130
[1] TRUE
[1] "2"
[1] 2131
[1] TRUE
[1] "2.5"
[1] 2132
[1] TRUE
[1] "2.5"
[1] 2133
[1] TRUE
[1] "2.5"
[1] 2134
[1] TRUE
[1] "2.7"
[1] 2135
[1] TRUE
[1] "2.7"
[1] 2136
[1] TRUE
[1] "2.7"
[1] 2137
[1] TRUE
[1] "2.5"
[1] 2138
[1] TRUE
[1] "2.5"
[1] 2139
[1] TRUE
[1] "2.5"
[1] 2140
[1] TRUE
[1] "100"
[1] 2141
[1] TRUE
[1] "100"
[1] 2142
[1] TRUE
[1] "100"
[1] 2143
[1] TRUE
[1] "100"
[1] 2144
[1] TRUE
[1] "100"
[1] 2145
[1] TRUE
[1] "100"
[1] 2146
[1] TRUE
[1] "100"
[1] 2147
[1] TRUE
[1] "100"
[1] 2148
[1] "11"
[1] 2149
[1] "11"
[1] 2150
[1] "0.2"
[1] 2151
[1] "0.2"
[1] 2152
[1] "2.6"
[1] 2153
[1] "2.6"
[1] 2154
[1] "0.2"
[1] 2155
[1] "0.2"
[1] 2156
[1] "3.2"
[1] 2157
[1] "3.2"
[1] 2158
[1] "0.6"
[1] 2159
[1] "6.1"
[1] 2160
[1] "6.1"
[1] 2161
[1] "11.5"
[1] 2162
[1] "11.5"
[1] 2163
[1] "14.7"
[1] 2164
[1] "0.2"
[1] 2165
[1] "0.2"
[1] 2166
[1] "0.2"
[1] 2167
[1] "0.2"
[1] 2168
[1] "5.4"
[1] 2169
[1] "5.4"
[1] 2170
[1] "24.1"
[1] 2171
[1] "0.3"
[1] 2172
[1] "0.3"
[1] 2173
[1] "0.6"
[1] 2174
[1] "0.6"
[1] 2175
[1] "25"
[1] 2176
[1] "25"
[1] 2177
[1] "14"
[1] 2178
[1] "14"
[1] 2179
[1] "9"
[1] 2180
[1] "2.5"
[1] 2181
[1] "2.5"
[1] 2182
[1] "1.1"
[1] 2183
[1] "1.1"
[1] 2184
[1] "0.3"
[1] 2185
[1] "0.3"
[1] 2186
[1] "0.3"
[1] 2187
[1] "1"
[1] 2188
[1] "1"
[1] 2189
[1] "2.8"
[1] 2190
[1] "2.8"
[1] 2191
[1] "2.8"
[1] 2192
[1] "3.3"
[1] 2193
[1] "3.3"
[1] 2194
[1] "0.6"
[1] 2195
[1] "0.6"
[1] 2196
[1] "32.5"
[1] 2197
[1] "32.5"
[1] 2198
[1] "30.5"
[1] 2199
[1] "2.6"
[1] 2200
[1] "1.4"
[1] 2201
[1] "1.4"
[1] 2202
[1] "0.6"
[1] 2203
[1] "1.8"
[1] 2204
[1] "0.8"
[1] 2205
[1] "2.4"
[1] 2206
[1] "2.4"
[1] 2207
[1] "4.4"
[1] 2208
[1] "2.8"
[1] 2209
[1] "0.9"
[1] 2210
[1] "1.4"
[1] 2211
[1] "13.1"
[1] 2212
[1] "2.3"
[1] 2213
[1] "0.6"
[1] 2214
[1] "16"
[1] 2215
[1] "5.5"
[1] 2216
[1] "1.8"
[1] 2217
[1] "20.5"
[1] 2218
[1] "0.3"
[1] 2219
[1] "1.9"
[1] 2220
[1] "0.4"
[1] 2221
[1] "18.9"
[1] 2222
[1] "1"
[1] 2223
[1] "0"
[1] 2224
[1] "0"
[1] 2225
[1] "2.4"
[1] 2226
[1] "2.4"
[1] 2227
[1] "0.3"
[1] 2228
[1] "0.3"
[1] 2229
[1] "7.1"
[1] 2230
[1] "7.1"
[1] 2231
[1] "0.5"
[1] 2232
[1] "0.5"
[1] 2233
[1] "5.2"
[1] 2234
[1] "5.2"
[1] 2235
[1] "1.3"
[1] 2236
[1] "12.8"
[1] 2237
[1] "12.9"
[1] 2238
[1] "6.8"
[1] 2239
[1] "4.3"
[1] 2240
[1] "10"
[1] 2241
[1] "10.1"
[1] 2242
[1] "0.4"
[1] 2243
[1] "0.1"
[1] 2244
[1] "0.1"
[1] 2245
[1] "11.4"
[1] 2246
[1] "2.8"
[1] 2247
[1] "6.5"
[1] 2248
[1] "13.7"
[1] 2249
[1] "5"
[1] 2250
[1] "4.5"
[1] 2251
[1] "3.8"
[1] 2252
[1] "6"
[1] 2253
[1] "10.2"
[1] 2254
[1] "3.8"
[1] 2255
[1] "6.7"
[1] 2256
[1] "6"
[1] 2257
[1] "0.2"
[1] 2258
[1] "8.1"
[1] 2259
[1] "1.2"
[1] 2260
[1] "9.2"
[1] 2261
[1] "1.4"
[1] 2262
[1] "6.9"
[1] 2263
[1] "1.7"
[1] 2264
[1] "1.7"
[1] 2265
[1] "0.8"
[1] 2266
[1] "3"
[1] 2267
[1] "9.2"
[1] 2268
[1] "14.4"
[1] 2269
[1] "15.2"
[1] 2270
[1] "17.3"
[1] 2271
[1] "12.4"
[1] 2272
[1] "16.4"
[1] 2273
[1] "13.6"
[1] 2274
[1] "20.4"
[1] 2275
[1] "5.6"
[1] 2276
[1] "7.2"
[1] 2277
[1] "14.2"
[1] 2278
[1] "8.2"
[1] 2279
[1] "13.5"
[1] 2280
[1] "11.4"
[1] 2281
[1] "21.9"
[1] 2282
[1] "13.6"
[1] 2283
[1] "10.6"
[1] 2284
[1] "7"
[1] 2285
[1] "2.7"
[1] 2286
[1] "1.9"
[1] 2287
[1] "1.1"
[1] 2288
[1] "2"
[1] 2289
[1] "2.9"
[1] 2290
[1] "1"
[1] 2291
[1] "16"
[1] 2292
[1] "18.8"
[1] 2293
[1] "2.5"
[1] 2294
[1] "21.2"
[1] 2295
[1] "13.3"
[1] 2296
[1] "5.1"
[1] 2297
[1] "14.7"
[1] 2298
[1] "20.9"
[1] 2299
[1] "1"
[1] 2300
[1] "1"
[1] 2301
[1] "19.4"
[1] 2302
[1] "11.9"
[1] 2303
[1] "16.6"
[1] 2304
[1] "16"
[1] 2305
[1] "4.3"
[1] 2306
[1] "1"
[1] 2307
[1] "5.1"
[1] 2308
[1] "4.3"
[1] 2309
[1] "17.7"
[1] 2310
[1] "19.8"
[1] 2311
[1] "6.1"
[1] 2312
[1] "6"
[1] 2313
[1] "0.5"
[1] 2314
[1] "0.5"
[1] 2315
[1] "7.1"
[1] 2316
[1] "8"
[1] 2317
[1] "7.6"
[1] 2318
[1] "1.4"
[1] 2319
[1] "1.4"
[1] 2320
[1] "0.4"
[1] 2321
[1] "23"
[1] 2322
[1] TRUE
[1] "100"
[1] 2323
[1] TRUE
[1] "100"
[1] 2324
[1] TRUE
[1] "100"
[1] 2325
[1] TRUE
[1] "100"
[1] 2326
[1] TRUE
[1] "100"
[1] 2327
[1] TRUE
[1] "100"
[1] 2328
[1] TRUE
[1] "100"
[1] 2329
[1] TRUE
[1] "100"
[1] 2330
[1] TRUE
[1] "3.23"
[1] 2331
[1] TRUE
[1] "3.23"
[1] 2332
[1] TRUE
[1] "3.23"
[1] 2333
[1] TRUE
[1] "3.23"
[1] 2334
[1] TRUE
[1] "0.9"
[1] 2335
[1] TRUE
[1] "0.9"
[1] 2336
[1] TRUE
[1] "5.6"
[1] 2337
[1] TRUE
[1] "5.6"
[1] 2338
[1] TRUE
[1] "0.7"
[1] 2339
[1] TRUE
[1] "0.7"
[1] 2340
[1] TRUE
[1] "0.7"
[1] 2341
[1] TRUE
[1] "1"
[1] 2342
[1] TRUE
[1] "1"
[1] 2343
[1] TRUE
[1] "1"
[1] 2344
[1] TRUE
[1] "1"
[1] 2345
[1] TRUE
[1] "1"
[1] 2346
[1] TRUE
[1] "1"
[1] 2347
[1] TRUE
[1] "7"
[1] 2348
[1] TRUE
[1] "7"
[1] 2349
[1] TRUE
[1] "7"
[1] 2350
[1] TRUE
[1] "1.1000000000000001"
[1] 2351
[1] TRUE
[1] "1.1000000000000001"
[1] 2352
[1] TRUE
[1] "1.1000000000000001"
[1] 2353
[1] TRUE
[1] "0.7"
[1] 2354
[1] TRUE
[1] "0.7"
[1] 2355
[1] TRUE
[1] "0.7"
[1] 2356
[1] TRUE
[1] "7.6"
[1] 2357
[1] TRUE
[1] "7.6"
[1] 2358
[1] TRUE
[1] "7.6"
[1] 2359
[1] TRUE
[1] "21.3"
[1] 2360
[1] TRUE
[1] "21.3"
[1] 2361
[1] TRUE
[1] "21.3"
[1] 2362
[1] TRUE
[1] "1.2"
[1] 2363
[1] TRUE
[1] "1.2"
[1] 2364
[1] TRUE
[1] "5.47"
[1] 2365
[1] TRUE
[1] "5.47"
[1] 2366
[1] TRUE
[1] "1.5"
[1] 2367
[1] TRUE
[1] "1.5"
[1] 2368
[1] TRUE
[1] "2.19"
[1] 2369
[1] TRUE
[1] "2.19"
[1] 2370
[1] TRUE
[1] "21.2"
[1] 2371
[1] TRUE
[1] "21.2"
[1] 2372
[1] TRUE
[1] "21.2"
[1] 2373
[1] TRUE
[1] "21.2"
[1] 2374
[1] TRUE
[1] "24.15"
[1] 2375
[1] TRUE
[1] "24.15"
[1] 2376
[1] TRUE
[1] "25.93"
[1] 2377
[1] TRUE
[1] "25.93"
[1] 2378
[1] TRUE
[1] "4.0999999999999996"
[1] 2379
[1] TRUE
[1] "4.0999999999999996"
[1] 2380
[1] TRUE
[1] "4.0999999999999996"
[1] 2381
[1] TRUE
[1] "4.0999999999999996"
[1] 2382
[1] TRUE
[1] "4.0999999999999996"
[1] 2383
[1] TRUE
[1] "4.0999999999999996"
[1] 2384
[1] TRUE
[1] "25.78"
[1] 2385
[1] TRUE
[1] "25.78"
[1] 2386
[1] TRUE
[1] "17.8"
[1] 2387
[1] TRUE
[1] "17.8"
[1] 2388
[1] TRUE
[1] "17.8"
[1] 2389
[1] TRUE
[1] "18.899999999999999"
[1] 2390
[1] TRUE
[1] "18.899999999999999"
[1] 2391
[1] TRUE
[1] "18.899999999999999"
[1] 2392
[1] TRUE
[1] "18.350000000000001"
[1] 2393
[1] TRUE
[1] "18.350000000000001"
[1] 2394
[1] TRUE
[1] "18.350000000000001"
[1] 2395
[1] TRUE
[1] "6"
[1] 2396
[1] TRUE
[1] "6"
[1] 2397
[1] TRUE
[1] "9.1999999999999993"
[1] 2398
[1] TRUE
[1] "9.1999999999999993"
[1] 2399
[1] TRUE
[1] "12.23"
[1] 2400
[1] TRUE
[1] "12.23"
[1] 2401
[1] TRUE
[1] "12.23"
[1] 2402
[1] TRUE
[1] "9.51"
[1] 2403
[1] TRUE
[1] "9.51"
[1] 2404
[1] TRUE
[1] "9.51"
[1] 2405
[1] TRUE
[1] "10.76"
[1] 2406
[1] TRUE
[1] "10.76"
[1] 2407
[1] TRUE
[1] "10.76"
[1] 2408
[1] TRUE
[1] "22.35"
[1] 2409
[1] TRUE
[1] "22.35"
[1] 2410
[1] TRUE
[1] "22.35"
[1] 2411
[1] TRUE
[1] "2.94"
[1] 2412
[1] TRUE
[1] "2.94"
[1] 2413
[1] TRUE
[1] "2.94"
[1] 2414
[1] TRUE
[1] "2.94"
[1] 2415
[1] TRUE
[1] "2.94"
[1] 2416
[1] TRUE
[1] "2.94"
[1] 2417
[1] TRUE
[1] "13.47"
[1] 2418
[1] TRUE
[1] "13.47"
[1] 2419
[1] TRUE
[1] "13.47"
[1] 2420
[1] TRUE
[1] "7.44"
[1] 2421
[1] TRUE
[1] "7.44"
[1] 2422
[1] TRUE
[1] "7.44"
[1] 2423
[1] TRUE
[1] "8.2100000000000009"
[1] 2424
[1] TRUE
[1] "8.2100000000000009"
[1] 2425
[1] TRUE
[1] "8.2100000000000009"
[1] 2426
[1] TRUE
[1] "9.8000000000000007"
[1] 2427
[1] TRUE
[1] "9.8000000000000007"
[1] 2428
[1] TRUE
[1] "9.8000000000000007"
[1] 2429
[1] TRUE
[1] "9.8000000000000007"
[1] 2430
[1] TRUE
[1] "9.8000000000000007"
[1] 2431
[1] TRUE
[1] "9.8000000000000007"
[1] 2432
[1] TRUE
[1] "7.6"
[1] 2433
[1] TRUE
[1] "7.6"
[1] 2434
[1] TRUE
[1] "7.6"
[1] 2435
[1] TRUE
[1] "7.6"
[1] 2436
[1] TRUE
[1] "0.6"
[1] 2437
[1] TRUE
[1] "0.6"
[1] 2438
[1] TRUE
[1] "0.6"
[1] 2439
[1] TRUE
[1] "0.6"
[1] 2440
[1] TRUE
[1] "1.6"
[1] 2441
[1] TRUE
[1] "1.6"
[1] 2442
[1] TRUE
[1] "1.6"
[1] 2443
[1] TRUE
[1] "1.6"
[1] 2444
[1] TRUE
[1] "1.1499999999999999"
[1] 2445
[1] TRUE
[1] "1.1499999999999999"
[1] 2446
[1] TRUE
[1] "1.1499999999999999"
[1] 2447
[1] TRUE
[1] "1.1499999999999999"
[1] 2448
[1] TRUE
[1] "3.4"
[1] 2449
[1] TRUE
[1] "3.4"
[1] 2450
[1] TRUE
[1] "3.4"
[1] 2451
[1] TRUE
[1] "3.4"
[1] 2452
[1] TRUE
[1] "0.8"
[1] 2453
[1] TRUE
[1] "0.8"
[1] 2454
[1] TRUE
[1] "0.8"
[1] 2455
[1] TRUE
[1] "0.8"
[1] 2456
[1] TRUE
[1] "4.9000000000000004"
[1] 2457
[1] TRUE
[1] "4.9000000000000004"
[1] 2458
[1] TRUE
[1] "4.9000000000000004"
[1] 2459
[1] TRUE
[1] "4.9000000000000004"
[1] 2460
[1] TRUE
[1] "4.9000000000000004"
[1] 2461
[1] TRUE
[1] "4.9000000000000004"
[1] 2462
[1] TRUE
[1] "1.2"
[1] 2463
[1] TRUE
[1] "1.2"
[1] 2464
[1] TRUE
[1] "1.2"
[1] 2465
[1] TRUE
[1] "1.2"
[1] 2466
[1] TRUE
[1] "0.78"
[1] 2467
[1] TRUE
[1] "0.78"
[1] 2468
[1] TRUE
[1] "0.78"
[1] 2469
[1] TRUE
[1] "0.78"
[1] 2470
[1] TRUE
[1] "1.2"
[1] 2471
[1] TRUE
[1] "1.2"
[1] 2472
[1] TRUE
[1] "1.2"
[1] 2473
[1] TRUE
[1] "1.2"
[1] 2474
[1] TRUE
[1] "1.2"
[1] 2475
[1] TRUE
[1] "1.2"
[1] 2476
[1] TRUE
[1] "0.97"
[1] 2477
[1] TRUE
[1] "0.97"
[1] 2478
[1] TRUE
[1] "0.97"
[1] 2479
[1] TRUE
[1] "0.97"
[1] 2480
[1] TRUE
[1] "0.97"
[1] 2481
[1] TRUE
[1] "0.97"
[1] 2482
[1] TRUE
[1] "1.2"
[1] 2483
[1] TRUE
[1] "1.2"
[1] 2484
[1] TRUE
[1] "1.2"
[1] 2485
[1] TRUE
[1] "1.2"
[1] 2486
[1] TRUE
[1] "2.1800000000000002"
[1] 2487
[1] TRUE
[1] "2.1800000000000002"
[1] 2488
[1] TRUE
[1] "2.1800000000000002"
[1] 2489
[1] TRUE
[1] "2.1800000000000002"
[1] 2490
[1] TRUE
[1] "2.1800000000000002"
[1] 2491
[1] TRUE
[1] "2.1800000000000002"
[1] 2492
[1] TRUE
[1] "11"
[1] 2493
[1] TRUE
[1] "11"
[1] 2494
[1] TRUE
[1] "11"
[1] 2495
[1] TRUE
[1] "11"
[1] 2496
[1] TRUE
[1] "11"
[1] 2497
[1] TRUE
[1] "11"
[1] 2498
[1] TRUE
[1] "0.8"
[1] 2499
[1] TRUE
[1] "0.8"
[1] 2500
[1] TRUE
[1] "0.8"
[1] 2501
[1] TRUE
[1] "0.8"
[1] 2502
[1] TRUE
[1] "1.21"
[1] 2503
[1] TRUE
[1] "1.21"
[1] 2504
[1] TRUE
[1] "1.21"
[1] 2505
[1] TRUE
[1] "1.21"
[1] 2506
[1] TRUE
[1] "0.64"
[1] 2507
[1] TRUE
[1] "0.64"
[1] 2508
[1] TRUE
[1] "0.64"
[1] 2509
[1] TRUE
[1] "0.64"
[1] 2510
[1] TRUE
[1] "0.42"
[1] 2511
[1] TRUE
[1] "0.42"
[1] 2512
[1] TRUE
[1] "0.42"
[1] 2513
[1] TRUE
[1] "0.42"
[1] 2514
[1] TRUE
[1] "1.06"
[1] 2515
[1] TRUE
[1] "1.06"
[1] 2516
[1] TRUE
[1] "1.06"
[1] 2517
[1] TRUE
[1] "1.06"
[1] 2518
[1] TRUE
[1] "2.12"
[1] 2519
[1] TRUE
[1] "2.12"
[1] 2520
[1] TRUE
[1] "3.12"
[1] 2521
[1] TRUE
[1] "3.12"
[1] 2522
[1] TRUE
[1] "0.44"
[1] 2523
[1] TRUE
[1] "0.44"
[1] 2524
[1] TRUE
[1] "0.44"
[1] 2525
[1] TRUE
[1] "0.44"
[1] 2526
[1] TRUE
[1] "3.03"
[1] 2527
[1] TRUE
[1] "3.03"
[1] 2528
[1] TRUE
[1] "3.03"
[1] 2529
[1] TRUE
[1] "5.63"
[1] 2530
[1] TRUE
[1] "5.63"
[1] 2531
[1] TRUE
[1] "5.63"
[1] 2532
[1] TRUE
[1] "5.63"
[1] 2533
[1] TRUE
[1] "2.5"
[1] 2534
[1] TRUE
[1] "2.5"
[1] 2535
[1] TRUE
[1] "1.6"
[1] 2536
[1] TRUE
[1] "1.6"
[1] 2537
[1] TRUE
[1] "3.63"
[1] 2538
[1] TRUE
[1] "3.63"
[1] 2539
[1] TRUE
[1] "3.63"
[1] 2540
[1] TRUE
[1] "3.63"
[1] 2541
[1] TRUE
[1] "2.8"
[1] 2542
[1] TRUE
[1] "2.8"
[1] 2543
[1] TRUE
[1] "2.8"
[1] 2544
[1] TRUE
[1] "2.8"
[1] 2545
[1] TRUE
[1] "0.81"
[1] 2546
[1] TRUE
[1] "0.81"
[1] 2547
[1] TRUE
[1] "0.81"
[1] 2548
[1] TRUE
[1] "0.81"
[1] 2549
[1] TRUE
[1] "2.91"
[1] 2550
[1] TRUE
[1] "2.91"
[1] 2551
[1] TRUE
[1] "2.91"
[1] 2552
[1] TRUE
[1] "1.2"
[1] 2553
[1] TRUE
[1] "1.2"
[1] 2554
[1] TRUE
[1] "1.2"
[1] 2555
[1] TRUE
[1] "1.2"
[1] 2556
[1] TRUE
[1] "2.5"
[1] 2557
[1] TRUE
[1] "2.5"
[1] 2558
[1] TRUE
[1] "2.5"
[1] 2559
[1] TRUE
[1] "2.5"
[1] 2560
[1] TRUE
[1] "12.05"
[1] 2561
[1] TRUE
[1] "12.05"
[1] 2562
[1] TRUE
[1] "12.05"
[1] 2563
[1] TRUE
[1] "12.05"
[1] 2564
[1] TRUE
[1] "1.5"
[1] 2565
[1] TRUE
[1] "1.5"
[1] 2566
[1] TRUE
[1] "1.5"
[1] 2567
[1] TRUE
[1] "1.5"
[1] 2568
[1] TRUE
[1] "2.98"
[1] 2569
[1] TRUE
[1] "2.98"
[1] 2570
[1] TRUE
[1] "2.98"
[1] 2571
[1] TRUE
[1] "0.6"
[1] 2572
[1] TRUE
[1] "0.6"
[1] 2573
[1] TRUE
[1] "0.95"
[1] 2574
[1] TRUE
[1] "0.95"
[1] 2575
[1] TRUE
[1] "0.95"
[1] 2576
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[1] 3840
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[1] 3841
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[1] 3845
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[1] 3846
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[1] 3847
[1] TRUE
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[1] 3851
[1] TRUE
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[1] 3852
[1] TRUE
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[1] 3853
[1] TRUE
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[1] 3854
[1] TRUE
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[1] 3855
[1] TRUE
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[1] 3856
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[1] 3857
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 3863
[1] TRUE
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[1] TRUE
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[1] 3871
[1] TRUE
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[1] 3872
[1] TRUE
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[1] 3873
[1] TRUE
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[1] 3890
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[1] TRUE
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[1] 3893
[1] TRUE
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[1] 3894
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[1] 3898
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[1] TRUE
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[1] TRUE
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[1] 3902
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[1] 3911
[1] TRUE
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[1] TRUE
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[1] 3913
[1] TRUE
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[1] 3914
[1] TRUE
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[1] 3915
[1] TRUE
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[1] 3916
[1] TRUE
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[1] 3917
[1] TRUE
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[1] 3918
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[1] 3921
[1] TRUE
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[1] 3922
[1] TRUE
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[1] 3923
[1] TRUE
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[1] 3924
[1] TRUE
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[1] 3925
[1] TRUE
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[1] 3926
[1] TRUE
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[1] 3927
[1] TRUE
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[1] 3928
[1] TRUE
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[1] 3929
[1] TRUE
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[1] 3930
[1] TRUE
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[1] 3931
[1] TRUE
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[1] 3932
[1] TRUE
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[1] 3933
[1] TRUE
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[1] 3934
[1] TRUE
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[1] 3935
[1] TRUE
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[1] 3936
[1] TRUE
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[1] 3937
[1] TRUE
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[1] 3938
[1] TRUE
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[1] 3939
[1] TRUE
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[1] 3940
[1] TRUE
[1] "11.59"
[1] 3941
[1] TRUE
[1] "11.59"
[1] 3942
[1] TRUE
[1] "11.59"
[1] 3943
[1] TRUE
[1] "12.37"
[1] 3944
[1] TRUE
[1] "12.37"
[1] 3945
[1] TRUE
[1] "12.37"
[1] 3946
[1] TRUE
[1] "13.88"
[1] 3947
[1] TRUE
[1] "13.88"
[1] 3948
[1] TRUE
[1] "13.88"
[1] 3949
[1] TRUE
[1] "13.88"
[1] 3950
[1] TRUE
[1] "13.88"
[1] 3951
[1] TRUE
[1] "13.88"
[1] 3952
[1] TRUE
[1] "13.88"
[1] 3953
[1] TRUE
[1] "13.88"
[1] 3954
[1] TRUE
[1] "13.88"
[1] 3955
[1] TRUE
[1] "13.88"
[1] 3956
[1] TRUE
[1] "13.88"
[1] 3957
[1] TRUE
[1] "13.88"
[1] 3958
[1] TRUE
[1] "0.64"
[1] 3959
[1] TRUE
[1] "0.64"
[1] 3960
[1] TRUE
[1] "0.64"
[1] 3961
[1] TRUE
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[1] 3962
[1] TRUE
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[1] 3963
[1] TRUE
[1] "1.06"
[1] 3964
[1] TRUE
[1] "1.06"
[1] 3965
[1] TRUE
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[1] 3966
[1] TRUE
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[1] 3967
[1] TRUE
[1] "13.89"
[1] 3968
[1] TRUE
[1] "13.89"
[1] 3969
[1] TRUE
[1] "13.89"
[1] 3970
[1] TRUE
[1] "13.89"
[1] 3971
[1] TRUE
[1] "13.89"
[1] 3972
[1] TRUE
[1] "13.89"
[1] 3973
[1] TRUE
[1] "13.89"
[1] 3974
[1] TRUE
[1] "13.89"
[1] 3975
[1] TRUE
[1] "13.89"
[1] 3976
[1] TRUE
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[1] 3977
[1] TRUE
[1] "13.89"
[1] 3978
[1] TRUE
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[1] 3979
[1] TRUE
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[1] 3980
[1] TRUE
[1] "17.81"
[1] 3981
[1] TRUE
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[1] 3982
[1] TRUE
[1] "6.3"
[1] 3983
[1] TRUE
[1] "6.3"
[1] 3984
[1] TRUE
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[1] 3985
[1] TRUE
[1] "2"
[1] 3986
[1] TRUE
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[1] 3987
[1] TRUE
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[1] 3988
[1] TRUE
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[1] TRUE
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[1] 3990
[1] TRUE
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[1] 3991
[1] TRUE
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[1] 3992
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 3998
[1] TRUE
[1] "7.89"
[1] 3999
[1] TRUE
[1] "7.89"
[1] 4000
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[1] 4001
[1] TRUE
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[1] 4002
[1] TRUE
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[1] 4003
[1] TRUE
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[1] 4004
[1] TRUE
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[1] 4005
[1] TRUE
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[1] 4006
[1] TRUE
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[1] 4007
[1] TRUE
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[1] 4008
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[1] 4010
[1] TRUE
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[1] 4011
[1] TRUE
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[1] 4012
[1] TRUE
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[1] 4013
[1] TRUE
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[1] 4014
[1] TRUE
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[1] 4015
[1] TRUE
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[1] 4016
[1] TRUE
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[1] 4017
[1] TRUE
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[1] 4018
[1] TRUE
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[1] 4019
[1] TRUE
[1] "6.3"
[1] 4020
[1] TRUE
[1] "6.32"
[1] 4021
[1] TRUE
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[1] 4022
[1] TRUE
[1] "6.32"
[1] 4023
[1] TRUE
[1] "6.73"
[1] 4024
[1] TRUE
[1] "6.73"
[1] 4025
[1] TRUE
[1] "6.73"
[1] 4026
[1] TRUE
[1] "6.73"
[1] 4027
[1] TRUE
[1] "3.79"
[1] 4028
[1] TRUE
[1] "3.79"
[1] 4029
[1] TRUE
[1] "3.79"
[1] 4030
[1] TRUE
[1] "3.79"
[1] 4031
[1] TRUE
[1] "4.86"
[1] 4032
[1] TRUE
[1] "4.86"
[1] 4033
[1] TRUE
[1] "1.54"
[1] 4034
[1] TRUE
[1] "1.54"
[1] 4035
[1] TRUE
[1] "1.54"
[1] 4036
[1] TRUE
[1] "1.54"
[1] 4037
[1] TRUE
[1] "1.87"
[1] 4038
[1] TRUE
[1] "1.87"
[1] 4039
[1] TRUE
[1] "0.91"
[1] 4040
[1] TRUE
[1] "0.91"
[1] 4041
[1] TRUE
[1] "0.91"
[1] 4042
[1] TRUE
[1] "0.91"
[1] 4043
[1] TRUE
[1] "0.92"
[1] 4044
[1] TRUE
[1] "0.92"
[1] 4045
[1] TRUE
[1] "0.92"
[1] 4046
[1] TRUE
[1] "0.92"
[1] 4047
[1] TRUE
[1] "1.18"
[1] 4048
[1] TRUE
[1] "1.18"
[1] 4049
[1] TRUE
[1] "0.69"
[1] 4050
[1] TRUE
[1] "0.69"
[1] 4051
[1] TRUE
[1] "0.69"
[1] 4052
[1] TRUE
[1] "0.69"
[1] 4053
[1] TRUE
[1] "0.88"
[1] 4054
[1] TRUE
[1] "0.88"
[1] 4055
[1] TRUE
[1] "1.22"
[1] 4056
[1] TRUE
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[1] 4057
[1] TRUE
[1] "1.22"
[1] 4058
[1] TRUE
[1] "1.22"
[1] 4059
[1] TRUE
[1] "0.98"
[1] 4060
[1] TRUE
[1] "0.98"
[1] 4061
[1] TRUE
[1] "0.98"
[1] 4062
[1] TRUE
[1] "0.98"
[1] 4063
[1] TRUE
[1] "1.18"
[1] 4064
[1] TRUE
[1] "1.18"
[1] 4065
[1] TRUE
[1] "9.47"
[1] 4066
[1] TRUE
[1] "9.47"
[1] 4067
[1] TRUE
[1] "9.47"
[1] 4068
[1] TRUE
[1] "9.47"
[1] 4069
[1] TRUE
[1] "9.47"
[1] 4070
[1] TRUE
[1] "9.47"
[1] 4071
[1] TRUE
[1] "9.47"
[1] 4072
[1] TRUE
[1] "9.47"
[1] 4073
[1] TRUE
[1] "9.47"
[1] 4074
[1] TRUE
[1] "9.47"
[1] 4075
[1] TRUE
[1] "9.47"
[1] 4076
[1] TRUE
[1] "9.47"
[1] 4077
[1] TRUE
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[1] 4078
[1] TRUE
[1] "12.14"
[1] 4079
[1] TRUE
[1] "12.14"
[1] 4080
[1] TRUE
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[1] TRUE
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[1] 4082
[1] TRUE
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[1] 4083
[1] TRUE
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[1] 4084
[1] TRUE
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[1] 4085
[1] TRUE
[1] "1.62"
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[1] TRUE
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[1] 4087
[1] TRUE
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[1] 4088
[1] TRUE
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[1] 4089
[1] TRUE
[1] "0.7"
[1] 4090
[1] TRUE
[1] "15.3"
[1] 4091
[1] TRUE
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[1] 4092
[1] TRUE
[1] "15.3"
[1] 4093
[1] TRUE
[1] "15.3"
[1] 4094
[1] TRUE
[1] "20.14"
[1] 4095
[1] TRUE
[1] "20.14"
[1] 4096
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[1] 4097
[1] TRUE
[1] "6.34"
[1] 4098
[1] TRUE
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[1] 4099
[1] TRUE
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[1] 4100
[1] TRUE
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[1] 4101
[1] TRUE
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[1] 4102
[1] TRUE
[1] "4.32"
[1] 4103
[1] TRUE
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[1] 4104
[1] TRUE
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[1] 4105
[1] TRUE
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[1] 4106
[1] TRUE
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[1] 4107
[1] TRUE
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[1] 4108
[1] TRUE
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[1] 4109
[1] TRUE
[1] "3.77"
[1] 4110
[1] TRUE
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[1] 4111
[1] TRUE
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[1] 4112
[1] TRUE
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[1] 4113
[1] TRUE
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[1] 4114
[1] TRUE
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[1] 4115
[1] TRUE
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[1] 4116
[1] TRUE
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[1] 4117
[1] TRUE
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[1] 4118
[1] TRUE
[1] "7.5"
[1] 4119
[1] TRUE
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[1] 4120
[1] TRUE
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[1] 4121
[1] TRUE
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[1] 4122
[1] TRUE
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[1] 4123
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[1] 4124
[1] TRUE
[1] "4.97"
[1] 4125
[1] TRUE
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[1] 4126
[1] TRUE
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[1] 4127
[1] TRUE
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[1] 4128
[1] TRUE
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[1] 4129
[1] TRUE
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[1] 4130
[1] TRUE
[1] "5.57"
[1] 4131
[1] TRUE
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[1] 4132
[1] TRUE
[1] "7.39"
[1] 4133
[1] TRUE
[1] "11.45"
[1] 4134
[1] TRUE
[1] "11.45"
[1] 4135
[1] TRUE
[1] "11.45"
[1] 4136
[1] TRUE
[1] "10.45"
[1] 4137
[1] TRUE
[1] "10.45"
[1] 4138
[1] TRUE
[1] "10.45"
[1] 4139
[1] TRUE
[1] "2.73"
[1] 4140
[1] TRUE
[1] "2.73"
[1] 4141
[1] TRUE
[1] "2.73"
[1] 4142
[1] TRUE
[1] "2.73"
[1] 4143
[1] TRUE
[1] "2"
[1] 4144
[1] TRUE
[1] "2"
[1] 4145
[1] TRUE
[1] "2"
[1] 4146
[1] TRUE
[1] "2"
[1] 4147
[1] TRUE
[1] "2.56"
[1] 4148
[1] TRUE
[1] "2.56"
[1] 4149
[1] TRUE
[1] "3.61"
[1] 4150
[1] TRUE
[1] "3.61"
[1] 4151
[1] TRUE
[1] "3.61"
[1] 4152
[1] TRUE
[1] "3.61"
[1] 4153
[1] TRUE
[1] "13.77"
[1] 4154
[1] TRUE
[1] "13.77"
[1] 4155
[1] TRUE
[1] "13.77"
[1] 4156
[1] TRUE
[1] "13.77"
[1] 4157
[1] TRUE
[1] "13.77"
[1] 4158
[1] TRUE
[1] "13.77"
[1] 4159
[1] TRUE
[1] "13.77"
[1] 4160
[1] TRUE
[1] "13.77"
[1] 4161
[1] TRUE
[1] "13.82"
[1] 4162
[1] TRUE
[1] "13.82"
[1] 4163
[1] TRUE
[1] "2.41"
[1] 4164
[1] TRUE
[1] "2.41"
[1] 4165
[1] TRUE
[1] "2.41"
[1] 4166
[1] TRUE
[1] "2.41"
[1] 4167
[1] TRUE
[1] "1.63"
[1] 4168
[1] TRUE
[1] "1.63"
[1] 4169
[1] TRUE
[1] "2.42"
[1] 4170
[1] TRUE
[1] "2.42"
[1] 4171
[1] TRUE
[1] "2.42"
[1] 4172
[1] TRUE
[1] "2.42"
[1] 4173
[1] TRUE
[1] "3.1"
[1] 4174
[1] TRUE
[1] "3.1"
[1] 4175
[1] TRUE
[1] "1.34"
[1] 4176
[1] TRUE
[1] "1.34"
[1] 4177
[1] TRUE
[1] "1.34"
[1] 4178
[1] TRUE
[1] "1.34"
[1] 4179
[1] TRUE
[1] "1.72"
[1] 4180
[1] TRUE
[1] "1.72"
[1] 4181
[1] TRUE
[1] "4.04"
[1] 4182
[1] TRUE
[1] "4.04"
[1] 4183
[1] TRUE
[1] "4.04"
[1] 4184
[1] TRUE
[1] "4.04"
[1] 4185
[1] TRUE
[1] "5.18"
[1] 4186
[1] TRUE
[1] "5.18"
[1] 4187
[1] TRUE
[1] "4.4"
[1] 4188
[1] TRUE
[1] "4.4"
[1] 4189
[1] TRUE
[1] "4.4"
[1] 4190
[1] TRUE
[1] "0.9"
[1] 4191
[1] TRUE
[1] "0.9"
[1] 4192
[1] TRUE
[1] "6.65"
[1] 4193
[1] TRUE
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[1] 4475
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[1] 4500
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[1] 4502
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[1] 4503
[1] TRUE
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[1] 4504
[1] TRUE
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[1] TRUE
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[1] 4506
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[1] TRUE
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[1] 4510
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[1] TRUE
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[1] 4512
[1] TRUE
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[1] TRUE
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[1] 4514
[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] TRUE
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[1] 4520
[1] TRUE
[1] "13.6"
[1] 4521
[1] TRUE
[1] "24.1"
[1] 4522
[1] TRUE
[1] "100"
[1] 4523
[1] TRUE
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[1] 4524
[1] TRUE
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[1] 4525
[1] TRUE
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[1] 4526
[1] TRUE
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[1] 4527
[1] TRUE
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[1] 4528
[1] TRUE
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[1] 4529
[1] TRUE
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[1] 4530
[1] TRUE
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[1] 4531
[1] TRUE
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[1] 4532
[1] TRUE
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[1] 4533
[1] TRUE
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[1] 4534
[1] TRUE
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[1] 4535
[1] TRUE
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[1] 4536
[1] TRUE
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[1] 4537
[1] TRUE
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[1] 4538
[1] TRUE
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[1] 4539
[1] TRUE
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[1] 4540
[1] TRUE
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[1] 4541
[1] TRUE
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[1] 4542
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[1] 4543
[1] TRUE
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[1] TRUE
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[1] 4545
[1] TRUE
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[1] 4546
[1] TRUE
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[1] 4547
[1] TRUE
[1] "25.08"
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[1] TRUE
[1] "7.26"
[1] 4549
[1] TRUE
[1] "7.26"
[1] 4550
[1] TRUE
[1] "2.84"
[1] 4551
[1] TRUE
[1] "2.84"
[1] 4552
[1] TRUE
[1] "2.84"
[1] 4553
[1] TRUE
[1] "2.84"
[1] 4554
[1] TRUE
[1] "10.17"
[1] 4555
[1] TRUE
[1] "8.26"
[1] 4556
[1] TRUE
[1] "8.26"
[1] 4557
[1] TRUE
[1] "2.73"
[1] 4558
[1] TRUE
[1] "2.73"
[1] 4559
[1] TRUE
[1] "1.88"
[1] 4560
[1] TRUE
[1] "14.65"
[1] 4561
[1] TRUE
[1] "0.3"
[1] 4562
[1] TRUE
[1] "1.4"
[1] 4563
[1] TRUE
[1] "1.4"
[1] 4564
[1] TRUE
[1] "0.5"
[1] 4565
[1] TRUE
[1] "4.8"
[1] 4566
[1] TRUE
[1] "1.9"
[1] 4567
[1] TRUE
[1] "1.9"
[1] 4568
[1] TRUE
[1] "1.9"
[1] 4569
[1] TRUE
[1] "1.9"
[1] 4570
[1] TRUE
[1] "2.3"
[1] 4571
[1] TRUE
[1] "2.3"
[1] 4572
[1] TRUE
[1] "2.7"
[1] 4573
[1] TRUE
[1] "2.7"
[1] 4574
[1] TRUE
[1] "2.3"
[1] 4575
[1] TRUE
[1] "2.3"
[1] 4576
[1] TRUE
[1] "9.7"
[1] 4577
[1] TRUE
[1] "9.7"
[1] 4578
[1] TRUE
[1] "9.7"
[1] 4579
[1] TRUE
[1] "4.4"
[1] 4580
[1] TRUE
[1] "4.4"
[1] 4581
[1] TRUE
[1] "4.4"
[1] 4582
[1] TRUE
[1] "4.4"
[1] 4583
[1] TRUE
[1] "4.4"
[1] 4584
[1] TRUE
[1] "4.4"
[1] 4585
[1] TRUE
[1] "5.3"
[1] 4586
[1] TRUE
[1] "5.3"
[1] 4587
[1] TRUE
[1] "5.3"
[1] 4588
[1] TRUE
[1] "6.4"
[1] 4589
[1] TRUE
[1] "6.4"
[1] 4590
[1] TRUE
[1] "6.4"
[1] 4591
[1] TRUE
[1] "5.3"
[1] 4592
[1] TRUE
[1] "5.3"
[1] 4593
[1] TRUE
[1] "5.3"
[1] 4594
[1] TRUE
[1] "0.6"
[1] 4595
[1] TRUE
[1] "0.6"
[1] 4596
[1] TRUE
[1] "0.6"
[1] 4597
[1] TRUE
[1] "0.6"
[1] 4598
[1] TRUE
[1] "0.8"
[1] 4599
[1] TRUE
[1] "0.8"
[1] 4600
[1] TRUE
[1] "0.8"
[1] 4601
[1] TRUE
[1] "0.8"
[1] 4602
[1] TRUE
[1] "0.8"
[1] 4603
[1] TRUE
[1] "0.8"
[1] 4604
[1] TRUE
[1] "5.4"
[1] 4605
[1] TRUE
[1] "5.4"
[1] 4606
[1] TRUE
[1] "5.4"
[1] 4607
[1] TRUE
[1] "5.4"
[1] 4608
[1] TRUE
[1] "5.4"
[1] 4609
[1] TRUE
[1] "5.4"
[1] 4610
[1] TRUE
[1] "7.7"
[1] 4611
[1] TRUE
[1] "7.7"
[1] 4612
[1] TRUE
[1] "7.7"
[1] 4613
[1] TRUE
[1] "5"
[1] 4614
[1] TRUE
[1] "5"
[1] 4615
[1] TRUE
[1] "5"
[1] 4616
[1] TRUE
[1] "4.7"
[1] 4617
[1] TRUE
[1] "4.7"
[1] 4618
[1] TRUE
[1] "4.7"
[1] 4619
[1] TRUE
[1] "0.9"
[1] 4620
[1] TRUE
[1] "0.9"
[1] 4621
[1] TRUE
[1] "0.9"
[1] 4622
[1] TRUE
[1] "0.9"
[1] 4623
[1] TRUE
[1] "0.9"
[1] 4624
[1] TRUE
[1] "0.9"
[1] 4625
[1] TRUE
[1] "1"
[1] 4626
[1] TRUE
[1] "1"
[1] 4627
[1] TRUE
[1] "1"
[1] 4628
[1] TRUE
[1] "1.2"
[1] 4629
[1] TRUE
[1] "1.2"
[1] 4630
[1] TRUE
[1] "1.2"
[1] 4631
[1] TRUE
[1] "1"
[1] 4632
[1] TRUE
[1] "1"
[1] 4633
[1] TRUE
[1] "1"
[1] 4634
[1] TRUE
[1] "1.2"
[1] 4635
[1] TRUE
[1] "1.2"
[1] 4636
[1] TRUE
[1] "1.2"
[1] 4637
[1] TRUE
[1] "1.2"
[1] 4638
[1] TRUE
[1] "1.4"
[1] 4639
[1] TRUE
[1] "1.4"
[1] 4640
[1] TRUE
[1] "1.7"
[1] 4641
[1] TRUE
[1] "1.7"
[1] 4642
[1] TRUE
[1] "1.4"
[1] 4643
[1] TRUE
[1] "1.4"
[1] 4644
[1] "5.2"
[1] 4645
[1] "5.2"
[1] 4646
[1] "5.2"
[1] 4647
[1] "5.2"
[1] 4648
[1] "6.3"
[1] 4649
[1] "6.3"
[1] 4650
[1] "4.4"
[1] 4651
[1] "4.4"
[1] 4652
[1] "4.9"
[1] 4653
[1] "4.9"
[1] 4654
[1] TRUE
[1] "3.8"
[1] 4655
[1] TRUE
[1] "3.8"
[1] 4656
[1] TRUE
[1] "3.8"
[1] 4657
[1] TRUE
[1] "3.8"
[1] 4658
[1] TRUE
[1] "4.6"
[1] 4659
[1] TRUE
[1] "4.6"
[1] 4660
[1] TRUE
[1] "5.5"
[1] 4661
[1] TRUE
[1] "5.5"
[1] 4662
[1] TRUE
[1] "4.6"
[1] 4663
[1] TRUE
[1] "4.6"
[1] 4664
[1] TRUE
[1] "1.3"
[1] 4665
[1] TRUE
[1] "1.3"
[1] 4666
[1] TRUE
[1] "1.5"
[1] 4667
[1] TRUE
[1] "1.5"
[1] 4668
[1] TRUE
[1] "4.6"
[1] 4669
[1] TRUE
[1] "4.6"
[1] 4670
[1] TRUE
[1] "4.6"
[1] 4671
[1] TRUE
[1] "4.6"
[1] 4672
[1] TRUE
[1] "4.6"
[1] 4673
[1] TRUE
[1] "4.6"
[1] 4674
[1] TRUE
[1] "6.6"
[1] 4675
[1] TRUE
[1] "6.6"
[1] 4676
[1] TRUE
[1] "6.6"
[1] 4677
[1] TRUE
[1] "5.8"
[1] 4678
[1] TRUE
[1] "5.8"
[1] 4679
[1] TRUE
[1] "5.8"
[1] 4680
[1] TRUE
[1] "6.6"
[1] 4681
[1] TRUE
[1] "6.6"
[1] 4682
[1] TRUE
[1] "6.6"
[1] 4683
[1] TRUE
[1] "6.3"
[1] 4684
[1] TRUE
[1] "6.3"
[1] 4685
[1] TRUE
[1] "6.3"
[1] 4686
[1] "1.3"
[1] 4687
[1] "1.3"
[1] 4688
[1] "1.3"
[1] 4689
[1] "1.3"
[1] 4690
[1] "1.3"
[1] 4691
[1] "1.3"
[1] 4692
[1] "1.6"
[1] 4693
[1] "1.6"
[1] 4694
[1] "1.6"
[1] 4695
[1] "1.9"
[1] 4696
[1] "1.9"
[1] 4697
[1] "1.9"
[1] 4698
[1] "1.6"
[1] 4699
[1] "1.6"
[1] 4700
[1] "1.6"
[1] 4701
[1] TRUE
[1] "5.5"
[1] 4702
[1] TRUE
[1] "5.5"
[1] 4703
[1] TRUE
[1] "6.6"
[1] 4704
[1] TRUE
[1] "6.6"
[1] 4705
[1] TRUE
[1] "5.5"
[1] 4706
[1] TRUE
[1] "5.5"
[1] 4707
[1] TRUE
[1] "1.3"
[1] 4708
[1] TRUE
[1] "1.3"
[1] 4709
[1] TRUE
[1] "1.3"
[1] 4710
[1] TRUE
[1] "1.3"
[1] 4711
[1] TRUE
[1] "1.6"
[1] 4712
[1] TRUE
[1] "1.6"
[1] 4713
[1] TRUE
[1] "1.9"
[1] 4714
[1] TRUE
[1] "1.9"
[1] 4715
[1] TRUE
[1] "1.6"
[1] 4716
[1] TRUE
[1] "1.6"
[1] 4717
[1] "1.3"
[1] 4718
[1] "1.3"
[1] 4719
[1] "1.3"
[1] 4720
[1] "1.3"
[1] 4721
[1] "1.6"
[1] 4722
[1] "1.6"
[1] 4723
[1] "1.9"
[1] 4724
[1] "1.9"
[1] 4725
[1] "1.6"
[1] 4726
[1] "1.6"
[1] 4727
[1] TRUE
[1] "3.5"
[1] 4728
[1] TRUE
[1] "3.5"
[1] 4729
[1] TRUE
[1] "3.5"
[1] 4730
[1] TRUE
[1] "3.5"
[1] 4731
[1] TRUE
[1] "4.2"
[1] 4732
[1] TRUE
[1] "4.2"
[1] 4733
[1] TRUE
[1] "5.1"
[1] 4734
[1] TRUE
[1] "5.1"
[1] 4735
[1] TRUE
[1] "4.2"
[1] 4736
[1] TRUE
[1] "4.2"
[1] 4737
[1] TRUE
[1] "1.2"
[1] 4738
[1] TRUE
[1] "1.2"
[1] 4739
[1] TRUE
[1] "1.2"
[1] 4740
[1] TRUE
[1] "1.2"
[1] 4741
[1] TRUE
[1] "1.4"
[1] 4742
[1] TRUE
[1] "1.4"
[1] 4743
[1] TRUE
[1] "1.7"
[1] 4744
[1] TRUE
[1] "1.7"
[1] 4745
[1] TRUE
[1] "1.4"
[1] 4746
[1] TRUE
[1] "1.4"
[1] 4747
[1] TRUE
[1] "15.5"
[1] 4748
[1] TRUE
[1] "15.5"
[1] 4749
[1] TRUE
[1] "15.5"
[1] 4750
[1] TRUE
[1] "3"
[1] 4751
[1] TRUE
[1] "3"
[1] 4752
[1] TRUE
[1] "3"
[1] 4753
[1] TRUE
[1] "3"
[1] 4754
[1] TRUE
[1] "3"
[1] 4755
[1] TRUE
[1] "3"
[1] 4756
[1] TRUE
[1] "3.6"
[1] 4757
[1] TRUE
[1] "3.6"
[1] 4758
[1] TRUE
[1] "3.6"
[1] 4759
[1] TRUE
[1] "4.4"
[1] 4760
[1] TRUE
[1] "4.4"
[1] 4761
[1] TRUE
[1] "4.4"
[1] 4762
[1] TRUE
[1] "3.6"
[1] 4763
[1] TRUE
[1] "3.6"
[1] 4764
[1] TRUE
[1] "3.6"
[1] 4765
[1] TRUE
[1] "0.4"
[1] 4766
[1] TRUE
[1] "0.4"
[1] 4767
[1] "1.1"
[1] 4768
[1] "1.1"
[1] 4769
[1] "1.1"
[1] 4770
[1] "1.1"
[1] 4771
[1] "1.6"
[1] 4772
[1] "1.6"
[1] 4773
[1] "1.3"
[1] 4774
[1] "1.3"
[1] 4775
[1] TRUE
[1] "4.2"
[1] 4776
[1] TRUE
[1] "1.4"
[1] 4777
[1] TRUE
[1] "1.4"
[1] 4778
[1] TRUE
[1] "1.7"
[1] 4779
[1] TRUE
[1] "1.7"
[1] 4780
[1] TRUE
[1] "0.9"
[1] 4781
[1] TRUE
[1] "0.9"
[1] 4782
[1] TRUE
[1] "1"
[1] 4783
[1] TRUE
[1] "1"
[1] 4784
[1] "1.5"
[1] 4785
[1] "1.5"
[1] 4786
[1] "1.5"
[1] 4787
[1] "1.5"
[1] 4788
[1] "1.7"
[1] 4789
[1] "1.7"
[1] 4790
[1] "1.9"
[1] 4791
[1] "1.9"
[1] 4792
[1] "1.7"
[1] 4793
[1] "1.7"
[1] 4794
[1] TRUE
[1] "2.2"
[1] 4795
[1] TRUE
[1] "2.2"
[1] 4796
[1] TRUE
[1] "2.2"
[1] 4797
[1] TRUE
[1] "9.4"
[1] 4798
[1] TRUE
[1] "9.4"
[1] 4799
[1] TRUE
[1] "9.4"
[1] 4800
[1] TRUE
[1] "2.6"
[1] 4801
[1] TRUE
[1] "2.6"
[1] 4802
[1] TRUE
[1] "2.6"
[1] 4803
[1] TRUE
[1] "2.6"
[1] 4804
[1] TRUE
[1] "2.6"
[1] 4805
[1] TRUE
[1] "2.6"
[1] 4806
[1] TRUE
[1] "3.3"
[1] 4807
[1] TRUE
[1] "3.3"
[1] 4808
[1] TRUE
[1] "3.3"
[1] 4809
[1] TRUE
[1] "3.5"
[1] 4810
[1] TRUE
[1] "3.5"
[1] 4811
[1] TRUE
[1] "3.5"
[1] 4812
[1] TRUE
[1] "3.3"
[1] 4813
[1] TRUE
[1] "3.3"
[1] 4814
[1] TRUE
[1] "3.3"
[1] 4815
[1] TRUE
[1] "1.2"
[1] 4816
[1] TRUE
[1] "1.2"
[1] 4817
[1] TRUE
[1] "2.1"
[1] 4818
[1] TRUE
[1] "2.1"
[1] 4819
[1] TRUE
[1] "2.1"
[1] 4820
[1] TRUE
[1] "2.1"
[1] 4821
[1] TRUE
[1] "3"
[1] 4822
[1] TRUE
[1] "3"
[1] 4823
[1] TRUE
[1] "2.5"
[1] 4824
[1] TRUE
[1] "2.5"

Step 5: Visual and statistical quality checks


Visualisation was performed in the harmonised data library for nutrient evaluation and missing values identification.

Including:

  • Identification of data gaps & errors. Systematically non-reported fishery products and/or food components.
  • Identification of missing values. For example, fishery products and food components were reported but high number of missing values were found.
  • Outliers. Values were extremely low or high compared within the same ICS SUA fishery category.

Step 6: Data formatting & exporting


  • Food component values were calculated for each ICS SUA fishery category.

  • Mean or re-calculation of food components were computed & evaluated.

  • Fishery data library was exported to excel.

The Extended Fisheries Global NCT (2022)

Fishery products per FCTs

Food composition data for fishery products: Overview (1)


  • 4824* fish and fishery products were compiled from 12 FCTs.

  • 9 FCTs were open and freely accessible and cleaned for all foods.

  • 30 food components were reported from more than 100 compiled variables.

Harmonisation of the data


  • Tagname re-naming was needed in all FCTs,

    • 4 FCTs did not report Tagnames (DK19, NZ18, BA13 & NO21)
  • “Lower quality values” were reported in 4 FCTs (JA15, KE18, WA19 & UF16).

  • Trace and/or below limit of detection values were transformed to zero in 6 FCTs (IN17 (“<LOD”), BA13, BR11, JA15 (“Tr”), KE18 & WA19 (“tr”)).

  • Measurement units transformation was needed for Tryptophan,Vitamin A, DHA and EPA in 3 FCTs (US19, IN17 & JA15).

Fisheries categories matching in Norwegian FCT (2021)

  • ISSCAAP code was allocated to 79% (183/232) of the fish items in NO21, of which

    • 89% (n=164) of the matches which were using the scientific names
    • 10% (n=19) were done manually using scientific name/ food description.
  • 95 ICS SUA fishery categories were matched to 234* fish and fishery products for use in the NCT.

Data Availability

Data Availability

Data Availability

Data Availability

Transformation & Harmonisation

  • 7 food components (SOP, Energy (kcal, kJ), Carbohydrates by difference, Vitamin A (RAE, RE) and Beta-Carotene Eq.) were re-calculated.

  • 4 food components (Ash, Beta-Carotene Eq., Retinol, and Niacin) were back-calculated.

  • 3 nutrients each (Fat, Thiamine and Vitamin B6) had multiple variables which were integrated.

Evaluating 10 nutrients based on data availability

Figure 1: Availability of data: Percentage (%) of fish entries with values of selected components.

Checking for ICS SUA category data gaps

Fish categories Data available (count) per fish category and nutrient
Water Vitamin B6 Vitamin B12 Selenium Niacin DHA EPA Copper
Aquatic animals nei, cured 3 3 3 0 3 3 3 3
Aquatic animals nei, fresh 8 8 8 5 8 5 5 5
Aquatic animals nei, preparations nei 2 2 2 0 2 2 2 2
Aquatic mammals, meat 15 4 4 4 7 4 4 7
Aquatic mammals, oils 5 2 3 3 5 3 3 3
Aquatic mammals, preparations nei 5 3 3 2 4 3 3 3
Aquatic plants 9 9 9 9 9 8 8 9
Aquatic plants, dried 24 24 24 12 24 8 18 24
Aquatic plants, preparations nei 10 9 9 5 10 4 5 10
Cephalopods, canned 1 1 1 0 1 1 1 1
Cephalopods, cured 4 4 4 0 4 4 4 4
Cephalopods, fresh 32 32 27 27 32 24 24 30
Cephalopods, frozen 33 33 28 28 33 25 25 31
Cephalopods, preparations nei 41 41 41 38 41 26 26 39
Crustaceans, canned 7 7 7 5 7 5 5 7
Crustaceans, cured 2 2 2 0 2 1 1 2
Crustaceans, fresh 88 80 69 70 81 45 42 87
Crustaceans, frozen 89 81 70 71 82 46 43 88
Crustaceans, preparations nei 116 114 114 100 116 59 53 113
Demersal fish, body oils 3 3 3 3 3 3 3 3
Demersal fish, canned 3 3 3 3 3 2 2 3
Demersal fish, cured 13 12 12 11 13 10 10 11
Demersal fish, fresh 154 152 107 108 154 146 146 153
Demersal fish, fresh fillets 86 85 85 81 86 57 57 85
Demersal fish, frozen, fillets 89 88 88 84 89 59 59 88
Demersal fish, frozen, whole 155 153 108 109 155 146 146 154
Demersal fish, liver oils 6 5 5 5 6 2 2 5
Demersal fish, preparations nei 248 242 235 204 248 177 178 240
Diadromous fish, body oils 1 1 1 1 1 1 1 1
Diadromous fish, canned 20 19 20 18 19 15 15 20
Diadromous fish, cured 21 20 20 19 20 17 17 20
Diadromous fish, fresh 60 56 53 47 59 52 52 56
Diadromous fish, fresh fillets 32 32 30 30 32 14 13 32
Diadromous fish, frozen fillets 32 32 30 30 32 14 13 32
Diadromous fish, frozen, whole 60 56 53 47 59 52 52 56
Diadromous fish, liver oils 6 5 5 5 6 2 2 5
Diadromous fish, preparations nei 116 110 111 96 116 70 70 110
Freshwater & diadromous fish, body oils 1 1 1 1 1 1 1 1
Freshwater & diadromous fish, canned 21 20 21 19 20 16 16 21
Freshwater & diadromous fish, cured 25 24 24 23 24 21 21 24
Freshwater & diadromous fish, fresh 127 122 83 78 125 88 88 123
Freshwater & diadromous fish, fresh fillets 79 75 64 56 79 32 29 75
Freshwater & diadromous fish, frozen fillets 85 81 68 60 85 36 33 81
Freshwater & diadromous fish, frozen, whole 129 124 83 78 127 88 88 125
Freshwater & diadromous fish, liver oils 6 5 5 5 6 2 2 5
Freshwater & diadromous fish, preparations nei 241 228 231 185 241 144 138 227
Freshwater fish, body oils 1 1 1 1 1 1 1 1
Freshwater fish, canned 1 1 1 1 1 1 1 1
Freshwater fish, cured 21 20 20 19 20 17 17 20
Freshwater fish, fresh 60 59 23 24 59 29 29 60
Freshwater fish, fresh fillets 47 43 34 26 47 18 16 43
Freshwater fish, frozen fillets 52 48 37 29 52 21 19 48
Freshwater fish, frozen, whole 62 61 23 24 61 29 29 62
Freshwater fish, liver oils 6 5 5 5 6 2 2 5
Freshwater fish, preparations nei 114 107 109 79 114 63 57 107
Marine fish nei, preparations nei 294 282 279 207 294 183 184 278
Marine fish nei, body oils 4 4 4 4 4 3 3 4
Marine fish nei, canned 67 66 65 40 67 51 51 65
Marine fish nei, cured 26 25 25 12 26 23 23 24
Marine fish nei, fresh 153 147 148 84 153 131 131 148
Marine fish nei, fresh fillets 136 135 133 119 136 87 84 135
Marine fish nei, frozen fillets 139 138 136 122 139 89 86 138
Marine fish nei, frozen, whole 154 148 149 85 154 131 131 149
Marine fish nei, liver oils 6 5 5 5 6 2 2 5
Molluscs, excluding cephalopods, canned 9 9 9 5 9 8 8 9
Molluscs, excluding cephalopods, cured 8 8 8 6 8 8 8 8
Molluscs, excluding cephalopods, fresh 82 82 77 63 82 49 49 82
Molluscs, excluding cephalopods, frozen 81 81 76 62 81 48 48 81
Other pelagic fish, body oils 3 3 3 3 3 3 3 3
Other pelagic fish, canned 30 30 29 20 30 23 23 30
Other pelagic fish, cured 2 2 2 1 2 2 2 2
Other pelagic fish, fresh 29 28 21 22 29 23 23 27
Other pelagic fish, fresh fillets 21 21 19 14 21 14 13 21
Other pelagic fish, frozen fillets 21 21 19 14 21 14 13 21
Other pelagic fish, frozen, whole 29 28 21 22 29 23 23 27
Other pelagic fish, liver oils 6 5 5 5 6 2 2 5
Other pelagic fish, preparations nei 22 19 22 11 22 11 11 19
Pelagic fish, body oils 3 3 3 3 3 3 3 3
Pelagic fish, canned 75 74 73 48 75 60 60 73
Pelagic fish, cured 22 22 22 10 22 22 22 21
Pelagic fish, fresh 98 94 64 75 98 83 83 94
Pelagic fish, fresh fillets 50 50 48 38 50 31 28 50
Pelagic fish, frozen fillets 50 50 48 38 50 31 28 50
Pelagic fish, frozen, whole 98 94 64 75 98 83 83 94
Pelagic fish, liver oils 6 5 5 5 6 2 2 5
Pelagic fish, preparations nei 103 97 101 60 103 63 63 95
Small pelagic fish, body oils 3 3 3 3 3 3 3 3
Small pelagic fish, canned 35 34 34 18 35 27 27 33
Small pelagic fish, cured 15 15 15 4 15 15 15 15
Small pelagic fish, fresh 61 58 35 46 61 53 53 59
Small pelagic fish, fresh fillets 30 30 30 25 30 17 15 30
Small pelagic fish, frozen fillets 30 30 30 25 30 17 15 30
Small pelagic fish, frozen, whole 61 58 35 46 61 53 53 59
Small pelagic fish, liver oils 6 5 5 5 6 2 2 5
Small pelagic fish, preparations nei 72 69 70 40 72 43 43 67

Selenium imputation and documentation (1)


  • The only missing data were selenium (Se) in four ICS SUA categories
  • Imputation was performed and documented in R(studio) (missing.R).
  • Documentation can be found in comment variable in the Fisheries Global NCT.


Selenium imputation and documentation (2)

food_desc SEmcg source_fct comment
Crustacean, Sakura shrimp, dried 101.69 JA15 SEmcg value from water adjusted value JA15 (10325)| CARTBEQmcg_std imputed with CARTBEQmcg
Mollusks, firefly squid, seasoned and smoked 131.19 JA15 SEmcg value from water adjusted, median values JA15(10342, 10345)| CARTBEQmcg_std imputed with CARTBEQmcg
Mollusks, processed squid, "Surume" dried squid 135.96 JA15 SEmcg value from water adjusted, median values JA15(10342, 10345) | CARTBEQmcg_std calculated from CARTBmcg, CARTAmcg and CRYPXBmcg
Mollusks, processed squid, "Saki-ika" dried, seasoned and shredded squid 125.39 JA15 SEmcg value from water adjusted, median values JA15(10342, 10345)| CARTBEQmcg_std imputed with CARTBEQmcg
Mollusks, processed squid, seasoned and smoked 96.26 JA15 SEmcg value from water adjusted, median values JA15(10342, 10345)| CARTBEQmcg_std imputed with CARTBEQmcg
Sea urchin, "Tsubu-uni" salted whole gonads 228.64 JA15 SEmcg value from water adjusted value JA15(10372)| CARTBEQmcg_std imputed with CARTBEQmcg
Sea urchin, "Neri-uni" salted whole gonad paste 222.47 JA15 SEmcg value from water adjusted value JA15(10372)| CARTBEQmcg_std imputed with CARTBEQmcg
Jellyfish, Salted, desalted 27.51 JA15 CHOAVLDFg_std assumed zero,SEmcg value from water adjusted value JA15(10372) | CARTBEQmcg_std calculated from CARTBmcg, CARTAmcg and CRYPXBmcg
Sea cucumber, "Konowata" salted and fermented viscera 93.92 JA15 SEmcg value from water adjusted value JA15(10372)| CARTBEQmcg_std imputed with CARTBEQmcg
Sea squirt, "Shiokara" salted and fermented meat and viscera [Syn. Ascidian] 96.29 JA15 SEmcg value from water adjusted value JA15(10372)| CARTBEQmcg_std imputed with CARTBEQmcg
Shrimp (crayfish), whole, dried 153.20 WA19 SEmcg value from water adjusted, median values UF16(092001, 092002, 092003, 092004) | CARTBEQmcg_std calculated from CARTBmcg, CARTAmcg and CRYPXBmcg

Missing values for Fatty Acids (DHA and EPA) by ICS SUA Fishery Category

Missing values for Fatty Acids (DHA and EPA) by ICS SUA Fishery Category

Visualisation for decision making: outliers

Checking outliers: Copper

source_fct fdc_id food_desc WATERg CUmg
JA15 10350 Mollusks, firefly squid, seasoned and smoked 23.00 12.00
UF16 093013 European flat oyster, flesh, raw 85.20 7.50
IN17 R003 Octopus 80.45 6.72
US19 11667 Seaweed, spirulina, dried 4.68 6.1
US19 15170 Mollusks, oyster, eastern, canned 85.14 4.461
NZ18 T8 Oyster, Pacific, flesh, raw 79.70 4.21
JA15 10371 Crustacean, mantis shrimp, boiled 77.20 3.46
JA15 10348 Mollusks, firefly squid, raw 83.00 3.42
IN17 Q006 Oyster 82.50 3.41
JA15 10325 Crustacean, Sakura shrimp, dried 19.40 3.34
JA15 10291 Mollusks, Escargot Apple snails, canned in brine 79.90 3.07
JA15 10349 Mollusks, firefly squid, boiled 78.10 2.97
JA15 10360 Mollusks, ocellated octopus, raw 83.20 2.96
UF16 093011 American cupped oyster, flesh, wild, raw (USA) 87.85 2.8580000000000001
US19 15167 Mollusks, oyster, eastern, wild, raw 89.04 2.858
IN17 S003 Freshwater Eel 75.57 2.72

Other visual & quality checks: Water


subset(fao_fish_fct, 
       str_detect(food_desc, " dry| dried")&
       !str_detect(food_desc, "stewed|cooked")&
         WATERg>30, 
       select = c(source_fct, food_desc,  WATERg)) %>% 
  distinct()
source_fct food_desc WATERg
JA15 Fish, Japanese sand lance, "Niboshi" boiled and dried whole 38.00
JA15 Fish, righteye flounder, dried 74.60
JA15 Fish, Pacific herring, "Migaki-nishin" dried fillet 60.60
JA15 Fish, Pacific herring, "Hirakiboshi" dried split 59.80
NO21 Salmon fillet, dry salted, with sugar and spices 61.00
US19 Walrus, meat, dried (Alaska Native) 38.80
US19 Seal, bearded (Oogruk), meat, dried, in oil (Alaska Native) 35.48

Other visual & quality checks: Sum of Proximate

Other visual & quality checks: Carbohydrates by difference

Documentation

  • A document with all the decisions and assumptions was generated.

  • In the NCT, comments were added as a separated column with all the data transformations: imputation, assumed zero, calculated, etc.

  • Scripts are provided to fully reproduce the approach and/or to update if needed.

The advantages of a scripted approach

Thomas Codd

The advantages of a scripted approach

  • Changes to the data are recorded in the script, not just in the data itself
  • Repeatable results (given the same input data and scripts have been run)
  • Easy to reapply processes after new data is integrated to the dataset
  • Produces a framework to solve a problem or produce a certain output, which can then be modified

How a scripted approach allows for fast updates (1)

An update can come in a number of forms:

  • Type 1: The data itself is updated, but the data format and structure remains the same
  • Type 2: The data is updated and the dataset is expanded (e.g. new columns added)
  • Type 3: The data is updated and/or expanded, and the data format or structure is altered

How a scripted approach allows for fast updates (2)

  • Type 1 solution: No change
  • Type 2 solution: Modify input script to account for new fields (renaming and reformatting etc)
  • Type 3 solution: Recreate input script to account for new data format

How a scripted approach allows for fast updates - the function aspect

  • Code segments of similar function can be repurposed for specific use-cases
  • Code segments can be written with express intent to complete a function with the least amount of adaptation required - functions
  • Several functions were written throughout this project, and tens of thousands of line of adaptable code

How a scripted approach allows for re-use of the FCT for new purposes through data interoperability

  • Once an FCT has been processed into an interoperable format the data can be re-used very easily, in R and out
  • Within R interoperable datasets can be combined with each other, subsetted, analysed, or adapted for other projects very easily
  • Exporting data to a csv and .xlsx document for use in other software is also easy to do

Reuse through interoperability - Example

Reuse through interoperability - Example

Documentation, and Collaborative Reproducibility with Git, GitHub & Sharepoint

Liberty Mlambo

Documentation, and Collaborative Reproducibility with Git, GitHub & Sharepoint


Fully reproducible data & documentation is provided within the repository available at https://github.com/LuciaSegovia/FAO-fisheries

A report covering: data curation steps, variable description, reference to external resources used, and describing any assumptions and challenges.(Available also in pdf)

All these steps would allow to replicate the process and to re-generate the table with the same format for future updates.

Conclusion & Recommendations for the future

What has been completed?


  • Expanded & updated the Fisheries Global NCT:

    • 21 + 6 additional food components for 95 ICS SUA Fisheries Categories.
    • Additional components were compiled but not reported.
    • 12 FCTs harmonised into a FCT data library.
  • A framework to reproduce and update FCTs and NCTs:

    • Re-usable scripts and functions.
    • Reproducible workflow & report.

An opportunity


  • For 12 FCTs all data was imported & harmonised in R(Studio).
  • The scripts from this project could be re-used to create NCTs for other foods.


  • “Easily” expanding with new FCTs into the NCT.

What needs to be further advanced?


  • Assessment of the impact of the assumptions on the final results (including imputation, calculation, assumed zero, and edible portion).

  • Improving the metadata of the FCTs and NCTs.

  • Better reporting of the food composition data.

    • Including methods of analysis, sampling and locations.
    • Avoiding ambiguous and non-standard reporting.
    • Transparent provenance of re-used data.


  • More analytical data prioritised for key foods and nutrients.

Better data for Nutrition


  • More food composition analysis are required, particularly for some fishery categories & nutrients.

    • “Aquatic Animals nei”, and fish oils in general.
    • DHA, EPA & Selenium.
  • High variability in certain nutrient concentration paired with high uncertainty.

    • Low number of values and highly variable.
    • Low or uncertain data quality.
    • Broad spectrum of fish and fishery products within one ICS SUA Fishery Category.

Improving data quality & reporting


  1. Imputation techniques and their overall impact on mean values should be evaluated.

  2. Food component description and combination of different methods of analysis (i.e., used for Vitamin B6).

  3. Other data management practices and their impact on final results (i.e., using “low quality values”, assuming zero for trace values, data recycling, etc.).

  4. FCT documentation for reproducibility, transparency and efficiency (i.e., minimum information standards, metadata, ontologies).

THANK YOU!

Q&A